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Questions and Answers

Cloud Kicks aims to use AI to improve sales processes and customer support. Which combination of capabilities would best achieve this?

  • Einstein Next Best Action and Case Auto Response Rules
  • Einstein Lead Scoring and Case Classification (correct)
  • Sales Path and Automated Case Escalations

A sales manager is looking to enhance the quality of lead data in their CRM. Which of these processes will most directly help the team to accomplish this goal?

  • Redesign the lead conversion process
  • Review and update missing lead information (correct)
  • Prioritize active leads quarterly.

Predictive analytics, machine learning, natural language processing (NLP), and computer vision are BEST described as:

  • Different types of automation tools used in Salesforce to streamline workflows.
  • Different types of data models used in Salesforce for storing information.
  • Different types of AI that can be applied in Salesforce to solve various business problems. (correct)

What is the MOST likely consequence of an organization neglecting data quality?

<p>Revenue loss, poor customer service, and reputational damage. (C)</p> Signup and view all the answers

Which of the following is the BEST approach to mitigate bias and ensure fairness in AI applications?

<p>Ongoing auditing and monitoring of data used in AI applications to detect and address potential biases. (A)</p> Signup and view all the answers

Cloud Kicks is concerned about data quality when predicting customer purchases using Einstein Prediction Builder. What is the quickest way to assess their data quality?

<p>Run reports to explore and analyze the data quality. (B)</p> Signup and view all the answers

A Salesforce administrator needs to create a field to capture the destination country for orders. Which field type is most suitable to ensure data quality and standardization?

<p>Picklist (C)</p> Signup and view all the answers

Cloud Kicks aims to reduce the workload on its customer care agents by implementing a chatbot on its website to answer FAQs. Which AI field is MOST appropriate for this?

<p>Natural language processing (A)</p> Signup and view all the answers

What should organizations prioritize to ensure data quality is sufficient for their AI initiatives?

<p>Collect and curate high-quality data from reliable sources. (C)</p> Signup and view all the answers

What is a likely consequence of utilizing poor-quality data when developing AI applications?

<p>AI models may produce biased or erroneous results. (A)</p> Signup and view all the answers

What is the MOST significant benefit of using a diverse, balanced, and large dataset for training an AI model?

<p>Enhanced model accuracy (B)</p> Signup and view all the answers

Cloud Kicks intends to use automated chat as its main support channel. Which Einstein feature is most appropriate for this?

<p>Bots (A)</p> Signup and view all the answers

Cloud Kicks is seeing low engagement with its latest email campaign. How can AI be used to improve customer email engagement?

<p>Create personalized emails (C)</p> Signup and view all the answers

In the context of AI, what is the primary function of Natural Language Processing (NLP)?

<p>To understand and generate human language (A)</p> Signup and view all the answers

How does a data quality assessment primarily benefit businesses that are using AI?

<p>Provides a benchmark for AI predictions (A)</p> Signup and view all the answers

Salesforce defines bias as classifying or marketing to individuals based on immutable traits. Which attribute exemplifies an immutable trait?

<p>Financial Status (C)</p> Signup and view all the answers

An organization is implementing AI models. Which data quality element is most crucial for ensuring the models' effectiveness?

<p>Consistency (A)</p> Signup and view all the answers

A sales manager wants to use AI in Salesforce to improve their processes. Which application of AI would provide the most direct benefit to their sales process?

<p>Lead scoring and opportunity forecasting (C)</p> Signup and view all the answers

How do data quality and transparency contribute to addressing bias in generative AI?

<p>They mitigate the chances of bias through better understanding and control. (A)</p> Signup and view all the answers

In the context of data quality, what does 'data completeness' primarily ensure?

<p>All required data points are present in the dataset. (D)</p> Signup and view all the answers

Cloud Kicks finds inconsistencies in how state and country values are recorded in their contact records. Which data quality dimension is most directly affected by this issue?

<p>Consistency (D)</p> Signup and view all the answers

What distinguishes predictive AI from generative AI in terms of their fundamental output?

<p>Predictive AI classifies inputs, while generative AI creates new content from inputs using machine learning. (C)</p> Signup and view all the answers

A business analyst checks for null values in attributes for a new AI use case. Which data quality component is the analyst primarily assessing?

<p>Completeness (C)</p> Signup and view all the answers

Which of the following is most likely to be considered a sensitive variable that could introduce bias into an AI model?

<p>Product preference (C)</p> Signup and view all the answers

Within Salesforce's Trusted AI Principles, what primary commitment does the Accountability principle emphasize?

<p>Assuming responsibility for actions and impacts on customers, partners, and society. (A)</p> Signup and view all the answers

How might skewed data inadvertently influence training data for AI models?

<p>By leading to biased outcomes favoring specific demographics present in the data. (C)</p> Signup and view all the answers

In what way might the reliance on AI in decision-making impact human intellect and critical thinking?

<p>It cultivates reliance on AI, which can diminish critical thinking and human analysis. (B)</p> Signup and view all the answers

Which combination represents three standard AI applications frequently utilized within Customer Relationship Management (CRM) systems?

<p>Predictive scoring, opportunity forecasting, personalized recommendations (A)</p> Signup and view all the answers

What role does transparency play in building trust and confidence in AI-driven CRM solutions?

<p>Transparency validates that AI recommendations are easy to understand and fully explainable. (B)</p> Signup and view all the answers

Which action exemplifies Salesforce's Trusted AI Principle of Inclusivity?

<p>Testing models using datasets that represent a wide range of demographics and scenarios. (A)</p> Signup and view all the answers

Cloud Kicks is experiencing issues with data quality due to outdated information. Which type of record most likely contributes to this problem?

<p>Incomplete records missing key data points. (A)</p> Signup and view all the answers

A consultant facilitates Consequence Scanning Workshops to ensure diverse datasets are used in AI model training. Which Salesforce Trusted AI Principle is being applied?

<p>Inclusivity (C)</p> Signup and view all the answers

Within Salesforce's Trusted AI Principles, what is the primary emphasis of the principle of Responsibility?

<p>Ensuring the ethical considerations guide the development and application of AI technologies. (C)</p> Signup and view all the answers

What is the most accurate statement regarding the role of user consent within AI data privacy practices?

<p>AI applications must obtain explicit user consent before collecting and utilizing personal data. (B)</p> Signup and view all the answers

Which practice is most effective in safeguarding customer data privacy when using AI-powered applications?

<p>Tracking and respecting customer data consent preferences regarding data usage. (B)</p> Signup and view all the answers

What is the primary benefit of ensuring explainability in trusted AI systems?

<p>It provides transparency into the AI's decision-making process, fostering trust and understanding. (B)</p> Signup and view all the answers

Cloud Kicks aims to integrate AI into its CRM to improve business operations. What initial step should the company take to prepare its data for AI utilization?

<p>Determine the specific business problems for which AI solutions will be developed. (B)</p> Signup and view all the answers

Flashcards

AI for Sales & Support

Using AI to improve sales and support.

Enhance Lead Data Quality

Improving the accuracy and completeness of lead information in a CRM system.

AI Fields

Types of AI including predicting outcomes, learning from data, understanding language, and interpreting images.

Consequences of Poor Data

Loss of revenue, poor customer experiences, and a damaged reputation.

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Mitigate AI Bias

Constantly checking and evaluating data utilized in AI applications to ensure impartiality.

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Natural Language Processing (NLP)

Helps computers understand and process human language.

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Ensure Data Quality

Collecting reliable data to improve the quality of AI applications.

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Benefit of large dataset

The better the outcome of the Model

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Assess Data Quality Quickly

Run reports to explore the data to understand how clean and complete it is.

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Picklist Field Type

Restricts user input to predefined options, ensuring consistency.

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Outcome of Poor Data Quality

AI models may produce biased or erroneous results if trained on poor-quality data.

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Einstein Bots

Automated chat powered by Einstein to assist customers.

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AI for Email Engagement

AI can analyze data to personalize emails, increasing engagement.

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Impact of Data Quality Assessment

Accurate data enables reliable AI predictions and recommendations.

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Immutable Trait Example

Characteristics like race are intrinsic and cannot be altered.

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Data Consistency

Ensuring data is standardized and uniform across systems.

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AI for Lead Scoring

Using AI to predict future sales trends and prioritize leads, improving sales efficiency.

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Mitigating Bias in AI

Implementing data quality checks reduces the risk of biased data influencing AI outputs.

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Data Completeness

Refers to all the necessary data values being present in a dataset, without missing entries.

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Non-uniform data values

State and country values need to be uniform, not having multiple variations to measure consistency.

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Predictive vs. Generative AI

Predictive AI classifies or forecasts outputs; generative AI creates new content.

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Verifying Data Completeness

It means checking that all expected fields have values, not blanks or nulls.

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Sensitive variables

Factors like gender can introduce bias if not handled carefully in data and models.

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Accountability (AI)

Taking responsibility for actions related to AI's impact on customers, partners, and society.

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Bias in AI training data

Data that is skewed towards one group or source.

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Challenge of Human-AI collaboration

Over-reliance on AI which reduces critical thinking.

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AI in CRM

Predicting outcomes, forecasting future results and recommending actions or products

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Predictive Scoring

Using past data to predict future outcomes or scores.

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Inclusivity in AI

Testing models with diverse datasets to ensure fair and equitable outcomes for all users.

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Duplicate Records

Records that contain redundant or identical information, leading to inaccuracies & inefficiency.

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Consequence Scanning

A consultant using workshops to test diverse datasets demonstrates commitment to inclusivity.

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Responsibility in AI

Focuses on the ethical and responsible deployment of AI, emphasizing accountability.

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User Consent in AI

AI's use of data is contingent on obtaining explicit permission from users.

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Safeguarding Data Privacy

Ensuring that customer data is protected through encryption and restricted access.

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Explainability of AI

The ability to comprehend how AI models arrive at specific conclusions or forecasts.

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Data availability

To optimize business operations by integrating AI into its CRM, start with data availability.

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Study Notes

  • Cloud Kicks aims to improve sales and customer support using AI. Einstein Lead Scoring and Case Classification are useful capabilities for them to use.
  • A sales manager can improve lead data quality in the CRM by redesigning the lead conversion process.
  • Predictive analytics, machine learning, natural language processing (NLP), and computer vision are different types of AI that can be applied in Salesforce.
  • Poor data quality can cause revenue loss, poor customer service, and reputational damage.
  • To mitigate bias and ensure fairness in AI applications, use ongoing auditing and monitoring of the data used.
  • Natural language processing (NLP) is suitable to implement a chatbot on its website that partially deflects incoming cases by answering frequently asked questions.
  • Organizations should collect and curate high-quality data from reliable sources to ensure data quality for their AI initiatives.
  • A diverse, balanced, and large dataset increases model accuracy.
  • A developer should focus on consistency to ensure the effectiveness of AI models when a large amount of data is scattered across different systems and is not standardized.
  • Lead scoring and opportunity forecasting is a beneficial AI application for a sales manager looking to improve processes using AI in Salesforce.
  • Data quality and transparency mitigate bias in generative AI.
  • Data completeness refers to the degree to which all required data points are present in the dataset.
  • Cloud Kicks discovered multiple variations of state and country values in contact records means there is an issue of inconsistency.
  • Predictive AI classifies or predicts outputs from input data while generative AI generates new and original output for a given input.
  • A business analyst (BA) is running a report to check for null values to verifiy completeness.
  • Gender is a sensitive variable that can lead to bias.
  • Salesforce's honesty guideline when training AI models exemplifies ensuring appropriate consent and transparency when using AI-generated responses.
  • Cloud Kicks wants to use Einstein Prediction Builder but has concerns on data quality, they can quickly assess data quality by running reports to explore the data quality.
  • A Salesforce administrator should use a picklist field type to ensure capture an order's destination country to ensure data quality.
  • A potential outcome of using poor-quality data in AI applications is AI models may produce biased or erroneous results.
  • Cloud Kicks plans to use automated chat as its primary support channel so they should use Einstein Bots.
  • AI can increase customer email engagement by creating personalized emails.
  • In the context of AI capabilities, natural language processing (NLP) is used to understand and generate human language.
  • A data quality assessment provides a benchmark for AI predictions and impacts business outcomes for companies using AI.
  • Financial status is a potentially sensitive attribute and is an example of one of Salesforce's immutable traits.
  • Ethical challenges associated with AI development includes human bias in machine learning algorithms and the lack of transparency AI decision-making processes.
  • Natural language processing (NLP) can enhance customer service agents' email responses by analyzing the written content of previous emails.
  • Testing models with diverse datasets is an example of Salesforce's Trusted AI Principle of Inclusivity in practice.
  • Duplicate records negatively impact data quality.
  • Inclusivity is the Salesforce Trusted AI Principle is practice when a consultant conducts a series of Consequence Scanning Workshops to support testing diverse datasets.
  • Ensuring ethical use of AI is the principle of Responsibility's main focus in the context of Salesforce's Trusted AI Principles.
  • AI infringes on privacy when user consent is not obtained regarding to AI data privacy.
  • Tracking customer data consent preferences is the best method to safeguard customer data privacy.
  • The significance of explainability of trusted AI systems describes how AI models make decisions.
  • Before incorporating AI into its CRM, Cloud Kicks should determine data availability.
  • Salesforce's principle of Empowerment aims to empower users of all skill levels to build AI applications with clicks, not code.
  • Taking responsibility for one's actions toward customers, partners, and society is the main focus of the Accountability principle in Salesforce's Trusted AI Principles.
  • The data is skewed toward a particular demographic or source and is a potential source of bias in training data for AI models.
  • A key challenge of human-AI collaboration in decision-making is that it creates reliance on AI, potentially leading to less critical thinking and oversight.
  • Predictive scoring, reporting, Einstein Bots are three commonly used examples of AI in CRM.

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