ai-900.pdf

Certy IQ Premium exam material Get certification quickly with the CertyIQ Premium exam material. Everything you need to prepare, learn & pass your certification exam easily. Lifetime free updates First attempt guaranteed success. https://www.CertyIQ.com  Microsoft...

Certy IQ Premium exam material Get certification quickly with the CertyIQ Premium exam material. Everything you need to prepare, learn & pass your certification exam easily. Lifetime free updates First attempt guaranteed success. https://www.CertyIQ.com  Microsoft (AI-900) Microsoft Azure AI Fundamentals Total: 244 Questions Link: https://certyiq.com/papers?provider=microsoft&exam=ai-900 Question: 1 CertyIQ A company employs a team of customer service agents to provide telephone and email support to customers. The company develops a webchat bot to provide automated answers to common customer queries. Which business benefit should the company expect as a result of creating the webchat bot solution? A. increased sales B. a reduced workload for the customer service agents C. improved product reliability Answer: B Explanation: Correct answer is B: a reduced workload for the customer service agents. Question: 2 CertyIQ For a machine learning progress, how should you split data for training and evaluation? A. Use features for training and labels for evaluation. B. Randomly split the data into rows for training and rows for evaluation. C. Use labels for training and features for evaluation. D. Randomly split the data into columns for training and columns for evaluation. Answer: B Explanation: You split rows not columns: The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data Question: 3 CertyIQ HOTSPOT - You are developing a model to predict events by using classification. You have a confusion matrix for the model scored on test data as shown in the following exhibit. Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point. Hot Area: Answer:  Explanation: Box 1: 11 - TP = True Positive. The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN). Box 2: 1,033 - FN = False Negative - Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Question: 4 CertyIQ You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do? A. Set Validation type to Auto. B. Enable Explain best model. C. Set Primary metric to accuracy. D. Set Max concurrent iterations to 0. Answer: B Explanation: Model Explain Ability. Most businesses run on trust and being able to open the ML black box helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs. Reference: https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-lear ning-service/ Question: 5 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer:  Explanation: A - Regression B - Anomly C - Cassification Anomaly detection encompasses many important tasks in machine learning: Identifying transactions that are potentially fraudulent. Learning patterns that indicate that a network intrusion has occurred. Finding abnormal clusters of patients. Checking values entered into a system. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection Question: 6 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:  Answer: Explanation: Reliability and safety: AI systems need to be reliable and safe in order to be trusted. It is important for a system to perform as it was originally designed and for it to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and champion/challenger methods should be integrated into the evaluation process. An AI system's performance can degrade over time, so a robust monitoring and model tracking process needs to be established to reactively and proactively measure the model's performance and retrain it, as necessary, to modernize it. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai Question: 7 CertyIQ DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer:  Explanation: Box 3: Natural language processing Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. What is Natural Language Processing? Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language. NLP enables you to create software that can: -Analyze text documents to extract key phrases and recognize entities (such as places, dates, or people). ie Text Analystics service -Perform sentiment analysis to determine how positive or negative the language used in a document is. ie Text Analytics service -Interpret spoken language, and synthesize speech responses. ie Speech service(speech to text and text to speech) -Automatically translate spoken or written phrases between languages. ie Text service(for text to text translation)/Speech service(for speech to text/speech translation) Interpret commands and determine appropriate actions. ie Language Understanding(LUIS) service Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing Question: 8 CertyIQ You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments. This is an example of which Microsoft guiding principle for responsible AI? A. fairness B. inclusiveness C. reliability and safety D. accountability  Answer: B Explanation: Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer. Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question: 9 CertyIQ DRAG DROP - Match the Microsoft guiding principles for responsible AI to the appropriate descriptions. To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: Reliability and safety - To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Box 2: Accountability - The people who design and deploy AI systems must be accountable for how their systems operate.  Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the final authority on any decision that impacts people's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems. Box 3: Privacy and security - As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question: 10 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question: 11 CertyIQ You are building an AI system. Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI? A. Ensure that all visuals have an associated text that can be read by a screen reader. B. Enable autoscaling to ensure that a service scales based on demand.  C. Provide documentation to help developers debug code. D. Ensure that a training dataset is representative of the population. Answer: C Explanation: Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question: 12 CertyIQ DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Keywords: Computer vision = identify (object) letters NLP = sentiment Anomaly Detection = fraud Machine Learning (regression) = predict Reference:  https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/ Question: 13 CertyIQ Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups. This an example of which Microsoft guiding principle for responsible AI? A. accountability B. fairness C. inclusiveness D. privacy and security Answer: C Explanation: C - Inclusiveness. No one is left out (disabled, gender, ethnicity, LGBTQIA+ etc etc) Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question: 14 CertyIQ What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. knowledgeability B. decisiveness C. inclusiveness D. fairness E. opinionatedness F. reliability and safety Answer: CDF Explanation: The six guiding principles are: 1. Fairness 2. Inclusiveness 3. Transparency 4. Privacy and Security 5. Reliability and Safety 6. Accountability Reference:  https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question: 15 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Object detection is correct. Semantic segmentation can seem tempting at first but that is more about classifying individual pixels based on their objects Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection Question: 16 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Answer is Feature Engineering. Explaination - feature engineering is applied first to generate additional features, and then feature selection is done to eliminate irrelevant, redundant, or highly correlated features. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features Question: 17 CertyIQ You run a charity event that involves posting photos of people wearing sunglasses on Twitter. You need to ensure that you only retweet photos that meet the following requirements: ✑ Include one or more faces. ✑ Contain at least one person wearing sunglasses. What should you use to analyze the images?  A. the Verify operation in the Face service B. the Detect operation in the Face service C. the Describe Image operation in the Computer Vision service D. the Analyze Image operation in the Computer Vision service Answer: B Explanation: Face detect can be requested to detect also glasses attribute Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview Question: 18 CertyIQ When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI? A. transparency B. inclusiveness C. fairness D. privacy and security Answer: A Explanation: Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Incorrect Answers: B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments. C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual. D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https:/ /docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai Question: 19 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: Box 1: Yes - Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Box 2: No - A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy. Box 3: No - Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai Question: 20 CertyIQ DRAG DROP - Match the principles of responsible AI to appropriate requirements. To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles 1. https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai 2. https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question: 21 CertyIQ DRAG DROP - You plan to deploy an Azure Machine Learning model as a service that will be used by client applications. Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order. Select and Place: Answer: Explanation: Data Prep Train Model Evaluate Model Reference:  https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines Question: 22 CertyIQ You are building an AI-based app. You need to ensure that the app uses the principles for responsible AI. Which two principles should you follow? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. Implement an Agile software development methodology B. Implement a process of AI model validation as part of the software review process C. Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer D. Prevent the disclosure of the use of AI-based algorithms for automated decision making Answer: BC Explanation: B ensures reliability and safety principle and C ensures privacy and security principle of AI. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/3-implications-responsible-ai- practical Question: 23 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer:  Explanation: Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai Question: 24 CertyIQ HOTSPOT - Select the answer that correctly completes the sentence. Hot Area: Answer: Explanation: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group  or individual. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai Question: 25 CertyIQ DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: Knowledge mining - You can use Azure Cognitive Search's knowledge mining results and populate your knowledge base of your chatbot. Box 2: Computer vision - Box 3: Natural language processing Natural language processing (NLP) is used for tasks such as sentiment analysis. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-proce ssing Question: 26 CertyIQ DRAG DROP - Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: Model evaluation - The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves. Box 2: Feature engineering - Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization. Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day. Box 3: Feature selection - In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance https://docs.m icrosoft.com/en-us/azure/machine-learning/concept-automated-ml Question: 27 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Reference: https://www.baeldung.com/cs/feature-vs-label https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-ge t-good-at-it/ Question: 28 CertyIQ You have the Predicted vs. True chart shown in the following exhibit. Which type of model is the chart used to evaluate? A. classification B. regression C. clustering Answer: B Explanation: What is a Predicted vs. True chart? Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m Question: 29 CertyIQ Which type of machine learning should you use to predict the number of gift cards that will be sold next month? A. classification B. regression C. clustering Answer: B Explanation: In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Reference:  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Question: 30 CertyIQ You have a dataset that contains information about taxi journeys that occurred during a given period. You need to train a model to predict the fare of a taxi journey. What should you use as a feature? A. the number of taxi journeys in the dataset B. the trip distance of individual taxi journeys C. the fare of individual taxi journeys D. the trip ID of individual taxi journeys Answer: B Explanation: The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label. Example: The provided data set contains the following columns: vendor_id: The ID of the taxi vendor is a feature. rate_code: The rate type of the taxi trip is a feature. passenger_count: The number of passengers on the trip is a feature. trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model. trip_distance: The distance of the trip is a feature. payment_type: The payment method (cash or credit card) is a feature. fare_amount: The total taxi fare paid is the label. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices Question: 31 CertyIQ You need to predict the sea level in meters for the next 10 years. Which type of machine learning should you use? A. classification B. regression C. clustering Answer: B Explanation: In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Question: 32 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: Box 1: Yes - Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Box 2: No - Box 3: Yes - During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms  and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment. Box 4: No - Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The label is the column you want to predict. Reference: https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features Question: 33 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False. classification as the prediction whether the loan will be repaid or not -- whether means class - categorical answer as yes or no - hence classification Question: 34 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: Box 1: Yes - In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing. Box 2: No - Box 3: No - Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier. Reference: https://www.cloudfactory.com/data-labeling-guide https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Question: 35 CertyIQ Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents? A. Form Recognizer B. Text Analytics C. Language Understanding D. Custom Vision Answer: A Explanation: Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/ Question: 36 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Accelerate your business processes by automating information extraction. Form Recognizer applies advanced  machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/ Question: 37 CertyIQ You use Azure Machine Learning designer to publish an inference pipeline. Which two parameters should you use to access the web service? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. the model name B. the training endpoint C. the authentication key D. the REST endpoint Answer: CD Explanation: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it. To consume the pipeline, you need: ✑ The REST endpoint for your service ✑ The Primary Key for your service Reference: https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/d eploy-service Question: 38 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:  Answer: Explanation: For Prod - AKS and for Dev/Test - use Azure Container Service To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint. Real-time endpoints must be deployed to an Azure Kubernetes Service cluster. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy Question: 39 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer:  Explanation: In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Incorrect Answers: ✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data. ✑ Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression http s://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-m odel-clustering Question: 40 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:  Answer: Explanation: Box 1: Yes - Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models. Box 2: Yes - With the designer you can connect the modules to create a pipeline draft. As you edit a pipeline in the designer, your progress is saved as a pipeline draft. Box 3: No - Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer Question: 41 CertyIQ HOTSPOT - You have the following dataset. You plan to use the dataset to train a model that will predict the house price categories of houses. What are Household Income and House Price Category? To answer, select the appropriate option in the answer area. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: Feature = Input. Label = Output. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results Question: 42 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer Question: 43 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:  Answer: Explanation: No - Automated machine learning only requires you to choose between Python SDK and studio web experience. Yes - Automated machine learning is a no code solution. No - This is done in the Azure Machine Learning studio web experience. Source: https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml Question: 44 CertyIQ A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types. You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person. This is an example of which type of machine learning? A. clustering B. regression C. classification Answer: C Explanation: This is classification. There are multiple reasons it's classification - The training data is already tagged as with the correct type of hemorrhage - Classification can be done for more than two classes (which people seem to not realize based on the comments) - You do clustering on a group of inputs. For example, the scans of 10 people. You can't cluster a single input. Clearly you get a new scan of a new patient and you want to know what that scan shows, you don't have a group of scans to cluster. - Clustering gives NO labels. You just get groups and don't know what the label is, but in this question it's very clear they want to know the label that belongs to the new scan -> classification Reference: https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning- designer/introduction Question: 45 CertyIQ When training a model, why should you randomly split the rows into separate subsets? A. to train the model twice to attain better accuracy B. to train multiple models simultaneously to attain better performance C. to test the model by using data that was not used to train the model Answer: C Explanation: First for training then evaluation. He is referring to the famous train_test_split that everyone uses to split the dataset into train and test sets. ​You lose statistical power by estimating on a subset n like 50 percent of original N. Thats the price you pay for splitting data. For example a normally independently distributed (n.i.d) x has an estimator of arithmetic mean X whose variance inversely related to N. Bigger sample is better for accuracy of a simple arithmetic mean. Question: 46 CertyIQ You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning. What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Use a graphical user interface (GUI) to run automated machine learning experiments. B. Create a compute instance to use as a workstation. C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer. D. Create a dataset from a comma-separated value (CSV) file. Answer: AC Explanation: Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace. Reference: https://www.azure.cn/en-us/pricing/details/machine-learning/ https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace Question: 47 CertyIQ You need to predict the income range of a given customer by using the following dataset. Which two fields should you use as features? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Education Level B. Last Name C. Age D. Income Range E. First Name Answer: AC Explanation: First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use. Question: 48 CertyIQ You are building a tool that will process images from retail stores and identify the products of competitors. The solution will use a custom model. Which Azure Cognitive Services service should you use?  A. Custom Vision B. Form Recognizer C. Face D. Computer Vision Answer: A Explanation: Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own image identifier models. An image identifier applies labels (which represent classifications or objects) to images, according to their detected visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify your own labels and train custom models to detect them. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overview Question: 49 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer:  Explanation: Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset Regression is a machine learning task that is used to predict the value of the label from a set of related features. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks Question: 50 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer:  Explanation: Box 1: No - The validation dataset is different from the test dataset that is held back from the training of the model. Box 2: Yes - A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model's hyperparameters. Box 3: No - The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset. Reference: https://machinelearningmastery.com/difference-test-validation-datasets/ Question: 51 CertyIQ What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. coefficient of determination (R2) B. F1 score C. root mean squared error (RMSE) D. area under curve (AUC) E. balanced accuracy Answer: AC Explanation: Small Tip: If its Regression model then remembers 'R' and the corresponding answer will be R2 and RMSE. A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative. C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is  being modeled. Incorrect Answers: B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model. D: aucROC or area under the curve (AUC) is used to evaluate a classification model. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics Question: 52 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Regression is a machine learning task that is used to predict the value of the label from a set of related features. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks Question: 53 CertyIQ DRAG DROP - You need to use Azure Machine Learning designer to build a model that will predict automobile prices. Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: Select Columns in Dataset For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns. Example:  Box 2: Split data - Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed. Box 3: Linear regression - Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score Question: 54 CertyIQ Which type of machine learning should you use to identify groups of people who have similar purchasing habits? A. classification B. regression C. clustering Answer: C Explanation:  Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks Question: 55 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Regression is a machine learning task that is used to predict the value of the label from a set of related features. Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks Question: 56 CertyIQ Which metric can you use to evaluate a classification model? A. true positive rate B. mean absolute error (MAE) C. coefficient of determination (R2) D. root mean squared error (RMSE) Answer: A Explanation: What does a good model look like? An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will  be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line. MAE, RMSE and R2 are metris for regression: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/evaluate-model Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification Question: 57 CertyIQ Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. dataset B. compute C. pipeline D. module Answer: AD Explanation: You can drag-and-drop datasets and modules onto the canvas. Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer Question: 58 CertyIQ You need to create a training dataset and validation dataset from an existing dataset. Which module in the Azure Machine Learning designer should you use? A. Select Columns in Dataset B. Add Rows C. Split Data D. Join Data Answer: C Explanation: A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits Question: 59 CertyIQ DRAG DROP - Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: Regression - In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Box 2: Clustering - Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment. Box 3: Classification - Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Question: 60 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Confidence is the right answer...."The probability score of the object classification (which you can interpret as the confidence of the predicted class being correct)"... Source: https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/1a-what-is- object-detection Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a- classifier Question: 61 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:  Answer: Explanation: Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai Question: 62 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation:  Feature engineering is the correct answer. "In Azure Machine Learning, data-scaling and normalization techniques are applied to make feature engineering easier. Collectively, these techniques and this feature engineering are called featurization in automated ML experiments." Feature selection is only about selection. Modifying features = Feature engineering https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-features Reference: https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features Question: 63 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data Question: 64 CertyIQ HOTSPOT - You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: Reference: https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selectio n Question: 65 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:  Answer: Explanation: Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/ 5-create-training-pipeline https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure -machine-learning-designer/introduction https://docs.microsoft.com/en-us/learn/modules/create-clustering- model-azure-machine-learning-designer/1-introduction Question: 66 CertyIQ Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. Calculate the accuracy of the model. B. Score test data by using the model. C. Combine multiple datasets. D. Use the model for real-time predictions. E. Remove records that have missing values. Answer: CE Explanation:  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion https://docs.microsoft.com /en-us/azure/architecture/data-science-process/prepare-data Question: 67 CertyIQ You need to predict the animal population of an area. Which Azure Machine Learning type should you use? A. regression B. clustering C. classification Answer: A Explanation: Regression is a supervised machine learning technique used to predict numeric values. Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/1 -introduction Question: 68 CertyIQ Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Python B. R C. C# D. Scala Answer: AB Explanation: Use Azure Machine Learning designer for customizing using Python and R code. "Build and train machine learning models with state-of-the art machine learning and deep learning algorithms, including those for computer vision, text analytics, recommendations, and anomaly detection. Drag and drop modules for no-code models or customize using Python and R code." as per the link given: https://azure.microsoft.com/en-us/products/machine-learning/designer/#features Reference: https://azure.microsoft.com/en-us/services/machine-learning/designer/#features Question: 69 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: Box 1: Yes - For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale. Box 2: No - K-Means Clustering - Because the K-means algorithm is an unsupervised learning method, a label column is optional. If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model. If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data. Box 3: No - For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/train-model https://docs.mic rosoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering Question: 70 CertyIQ Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items. Which type of AI workload should the company use? A. anomaly detection B. conversational AI C. computer vision D. natural language processing Answer: C Explanation: Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview Question: 71 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer:  Explanation: Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector Question: 72 CertyIQ In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Extract the invoice number from an invoice. B. Translate a form from French to English. C. Find image of product in a catalog. D. Identify the retailer from a receipt. Answer: AD Explanation: Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features Question: 73 CertyIQ HOTSPOT - Select the answer that correctly completes the sentence. Hot Area:  Answer: Explanation: Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview https://docs.microsoft.c om/en-us/azure/cognitive-services/computer-vision/intro-to-spatial-analysis-public-preview Question: 74 CertyIQ HOTSPOT - You have a database that contains a list of employees and their photos. You are tagging new photos of the employees. For each of the following statements select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:  Answer: Explanation: Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en-us/az ure/cognitive-services/face/concepts/face-detection Question: 75 CertyIQ You need to develop a mobile app for employees to scan and store their expenses while travelling. Which type of computer vision should you use? A. semantic segmentation B. image classification C. object detection D. optical character recognition (OCR) Answer: D  Explanation: Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text Question: 76 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: Box 1: Yes - Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found.  Box 2: Yes - The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Box 3: No - Custom Vision service can be used only on graphic files. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview Question: 77 CertyIQ You are processing photos of runners in a race. You need to read the numbers on the runners' shirts to identity the runners in the photos. Which type of computer vision should you use? A. facial recognition B. optical character recognition (OCR) C. image classification D. object detection Answer: B Explanation: Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr Question: 78 CertyIQ DRAG DROP - Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:  Answer: Explanation: Box 1: Image classification - Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Box 2: Object detection - Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images. Box 3: Semantic Segmentation - Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region. Reference: https://developers.google.com/machine-learning/practica/image-classification https://docs.microsoft.com/en -us/dotnet/machine-learning/tutorials/object-detection-model-builder https://nanonets.com/blog/how-to-do- semantic-segmentation-using-deep-learning/ Question: 79 CertyIQ You use drones to identify where weeds grow between rows of crops to send an instruction for the removal of the weeds. This is an example of which type of computer vision? A. object detection B. optical character recognition (OCR) C. scene segmentation Answer: A Explanation: Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. Incorrect Answers: B: Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents. C: Scene segmentation determines when a scene changes in video based on visual cues. A scene depicts a  single event and it's composed by a series of consecutive shots, which are semantically related. Reference: https://docs.microsoft.com/en-us/ai-builder/object-detection-overview https://docs.microsoft.com/en-us/azur e/cognitive-services/computer-vision/overview-ocr https://docs.microsoft.com/en-us/azure/azure-video-analy zer/video-analyzer-for-media-docs/video-indexer-overview Question: 80 CertyIQ DRAG DROP - Match the facial recognition tasks to the appropriate questions. To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: verification - Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score. Box 2: similarity - Box 3: Grouping - Box 4: identification - Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.  ~Face verification: The Verify API does an authentication against two detected faces or from one detected face to one person object. Practically, it evaluates whether two faces belong to the same person. ~Person identification: The Identify API is used to identify a detected face against a database of people (facial recognition search). This feature might be useful for automatic image tagging in photo management software. You create the database in advance, and you can edit it over time. Tips to remember: - verification = same person? similarity = look like? grouping = belong together? identification = who is this person? Reference: https://azure.microsoft.com/en-us/services/cognitive-services/face/#features Question: 81 CertyIQ DRAG DROP - Match the types of computer vision workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer:  Explanation: Box 1: Facial recognition - Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images. Box 2: OCR - Box 3: Objection detection - Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image. The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/face/ https://docs.microsoft.com/en-us/azure/c ognitive-services/computer-vision/concept-object-detection Question: 82 CertyIQ You need to determine the location of cars in an image so that you can estimate the distance between the cars. Which type of computer vision should you use? A. optical character recognition (OCR) B. object detection C. image classification D. face detection Answer: B Explanation: Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image. The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection Question: 83 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer: Explanation: Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply. Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use. Incorrect Answers: Computer Vision: Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home Question: 84 CertyIQ You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit. Which type of computer vision was used? A. object detection B. face detection C. optical character recognition (OCR) D. image classification Answer: A Explanation: Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image. The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection Question: 85 CertyIQ What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Train a custom image classification model.  B. Detect faces in an image. C. Recognize handwritten text. D. Translate the text in an image between languages. Answer: BC Explanation: B: Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces. C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home Question: 86 CertyIQ What is a use case for classification? A. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night. B. analyzing the contents of images and grouping images that have similar colors C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work D. predicting how many minutes it will take someone to run a race based on past race times Answer: C Explanation: Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False. Incorrect Answers: A: This is Regression. B: This is Clustering. D: This is Regression. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression http s://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-m odel-clustering Question: 87 CertyIQ What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Predict stock prices. B. Detect brands in an image. C. Detect the color scheme in an image D. Translate text between languages. E. Extract key phrases.  Answer: BC Explanation: B: Identify commercial brands in images or videos from a database of thousands of global logos. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement. C: Analyze color usage within an image. Computer Vision can determine whether an image is black & white or color and, for color images, identify the dominant and accent colors. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview Question: 88 CertyIQ You need to build an image tagging solution for social media that tags images of your friends automatically. Which Azure Cognitive Services service should you use? A. Face B. Form Recognizer C. Text Analytics D. Computer Vision Answer: A Explanation: More aligned towards face Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview https://docs.microsoft.com/en- us/azure/cognitive-services/face/face-api-how-to-topics/howtodetectfacesinimage Question: 89 CertyIQ In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Identify the retailer from a receipt B. Translate from French to English C. Extract the invoice number from an invoice D. Find images of products in a catalog Answer: AC Explanation: Reference: https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/overview?tabs=v2-1 Question: 90 CertyIQ DRAG DROP - Match the facial recognition tasks to the appropriate questions. To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: verification - Identity verification - Modern enterprises and apps can use the Face identification and Face verification operations to verify that a user is who they claim to be. Box 2: similarity - The Find Similar operation does face matching between a target face and a set of candidate faces, finding a smaller set of faces that look similar to the target face. This is useful for doing a face search by image. The service supports two working modes, matchPerson and matchFace. The matchPerson mode returns similar faces after filtering for the same person by using the Verify API. The matchFace mode ignores the same-person filter. It returns a list of similar candidate faces that may or may not belong to the same person. Box 3: identification - Face identification can address "one-to-many" matching of one face in an image to a set of faces in a secure repository. Match candidates are returned based on how closely their face data matches the query face. This scenario is used in granting building or airport access to a certain group of people or verifying the user of a device. Reference:  https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview Question: 91 CertyIQ Which Computer Vision feature can you use to generate automatic captions for digital photographs? A. Recognize text. B. Identify the areas of interest. C. Detect objects. D. Describe the images. Answer: D Explanation: Describe images with human-readable language Computer Vision can analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence. The image description feature is part of the Analyze Image API. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-describing-images Question: 92 CertyIQ Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents? A. Custom Vision B. Face C. Form Recognizer D. Language Answer: C Explanation: Form Recognizer applies advanced machine learning to accurately extract text, key-value pairs, tables, and structures from documents. Reference: https://azure.microsoft.com/en-us/services/form-recognizer/ Question: 93 CertyIQ HOTSPOT - Select the answer that correctly completes the sentence. Hot Area:  Answer: Explanation: Handwriting OCR (optical character recognition) is the process of automatically extracting handwritten information from paper, scans and other low-quality digital documents. Reference: https://vidado.ai/handwriting-ocr Question: 94 CertyIQ You are developing a solution that uses the Text Analytics service. You need to identify the main talking points in a collection of documents. Which type of natural language processing should you use? A. entity recognition B. key phrase extraction C. sentiment analysis D. language detection Answer: B Explanation: Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-proce ssing Question: 95 CertyIQ In which two scenarios can you use speech recognition? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. an in-car system that reads text messages aloud B. providing closed captions for recorded or live videos C. creating an automated public address system for a train station D. creating a transcript of a telephone call or meeting Answer: BD Explanation: B, D is correct as LUIS interpret meaning of text whereas text analytics is for sentiment or key Phrase extraction. Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Question: 96 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer:  Explanation: Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Question: 97 CertyIQ You need to build an app that will read recipe instructions aloud to support users who have reduced vision. Which version service should you use? A. Text Analytics B. Translator C. Speech D. Language Understanding (LUIS) Answer: C Explanation: Speech is actually Text-to-Speech Speech Recognition is actually Speech-to-Text. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#features Question: 98 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:  Answer: Explanation: Transcribe a call to text -Speech Service: Speech to Text Service Extract call Transcription to find key entity - Text Analytic : Entity Recognition Translate a call to different language : Speech Service : Speech Translations Thus Y/ Y /Y Reference: https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/ Question: 99 CertyIQ Your website has a chatbot to assist customers. You need to detect when a customer is upset based on what the customer types in the chatbot. Which type of AI workload should you use? A. anomaly detection B. computer vision C. regression D. natural language processing Answer: D Explanation: Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-proce ssing Question: 100 CertyIQ You plan to develop a bot that will enable users to query a knowledge base by using natural language processing. Which two services should you include in the solution? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. QnA Maker B. Azure Bot Service C. Form Recognizer D. Anomaly Detector Answer: AB Explanation: Reference: https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-servic e-4.0 https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-se rvice Question: 101 CertyIQ In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad B. generating live captions for a news broadcast C. extracting key phrases from the audio recording of a meeting D. an AI character in a computer game that speaks audibly to a player  Answer: AD Explanation: Azure Text to Speech is a Speech service feature that converts text to lifelike speech. Incorrect Answers: C: Extracting key phrases is not speech synthesis. Reference: https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/ Question: 102 CertyIQ HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area: Answer: Explanation: The translator service provides multi-language support for text translation, transliteration, language detection, and dictionaries. Speech-to-Text, also known as automatic speech recognition (ASR), is a feature of Speech Services that provides transcription.  Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/translator-info-overview https://docs.mi crosoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-note Question: 103 CertyIQ DRAG DROP - You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book. Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place: Answer: Explanation: Box 1: Entity recognition - the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text. Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as: ✑ Which companies were mentioned in a news article? ✑ Does a tweet contain the name of a person? Does the tweet also provide his current location? ✑ Were specified products mentioned in complaints or reviews?  Box 2: Sentiment Analysis - The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative", "neutral" and "positive") and confidence scores at the sentence and document-level. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sent iment-analysis Question: 104 CertyIQ You are building a knowledge base by using QnA Maker. Which file format can you use to populate the knowledge base? A. PPTX B. XML C. ZIP D. PDF Answer: D Explanation: D: Content types of documents you can add to a knowledge base: Content types include many standard structured documents such as PDF, DOC, and TXT. Note: The tool supports the following file formats for ingestion: ✑.tsv: QnA contained in the format Question(tab)Answer. ✑.txt,.docx,.pdf: QnA contained as regular FAQ content--that is, a sequence of questions and answers. Incorrect Answers: A: PPTX is the default presentation file format for new PowerPoint presentations. B: It is not possible to ingest xml file directly. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/data-sources-and-content Question: 105 CertyIQ In which scenario should you use key phrase extraction? A. identifying whether reviews of a restaurant are positive or negative B. generating captions for a video based on the audio track C. identifying which documents provide information about the same topics D. translating a set of documents from English to German  Answer: C Explanation: C - "Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff"." Question: 106 CertyIQ You have insurance claim reports that are stored as text. You need to extract key terms from the reports to generate summaries. Which type of AI workload should you use? A. natural language processing B. conversational AI C. anomaly detection D. computer vision Answer: A Explanation: NLP can extract key terms from the reports to generate summaries. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing Question: 107 CertyIQ HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area: Answer:  Explanation: Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-proce ssing Question: 108 CertyIQ Which AI service can you use to interpret the meaning of a user input such as `Call me back later?` A. Translator B. Text Analytics C. Speech D. Language Understanding (LUIS) Answer: D Explanation: Language Understanding (LUIS) is a cloud-based AI service, that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis Question: 109 CertyIQ You are developing a chatbot solution in Azure. Which service should you use to determine a user's intent? A. Translator B. QnA Maker C. Speech D. Language Understanding (LUIS)  Answer: D Explanation: Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis Question: 110 CertyIQ You need to make the written press releases of your company available in a range of languages. Which service should you use? A. Translator B. Text Analytics C. Speech D. Language Understanding (LUIS) Answer: A Explan