AWS AI Practitioner Exam - ML Project Phases

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Questions and Answers

Which phase involves transforming data into features that are beneficial for machine learning models?

  • Model Evaluation
  • Model Training
  • Data Collection
  • Feature Engineering (correct)

In which phase are the model's parameters adjusted to optimize its performance?

  • Model Training (correct)
  • Model Evaluation
  • Model Deployment
  • Data Augmentation

What is the primary purpose of data augmentation?

  • Improving model accuracy by adding more data.
  • Testing the model on a larger dataset.
  • Enhancing features by adding new data.
  • Generating synthetic data to overcome data scarcity. (correct)

During which phase do you assess the model's performance on unseen data?

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

What is the primary objective of monitoring and debugging a deployed model?

<p>Ensuring ongoing model performance and addressing issues. (B)</p> Signup and view all the answers

Which of these is NOT considered a phase in a typical machine learning project?

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

Which of these is a potential cause for model drift?

<p>Changing data distributions over time (D)</p> Signup and view all the answers

What is the significance of evaluating the model on a test dataset?

<p>To identify how well the model performs on unseen data. (A)</p> Signup and view all the answers

What is the primary purpose of retraining a machine learning model?

<p>To improve the model's performance by incorporating new data and adjusting parameters. (A)</p> Signup and view all the answers

Why is it crucial to understand the correlation between variables during exploratory data analysis?

<p>To determine which variables are most influential in the model. (B)</p> Signup and view all the answers

What is the main objective of deploying a machine learning model?

<p>To make predictions on new data and provide insights to users. (C)</p> Signup and view all the answers

Why is it important to monitor a deployed machine learning model?

<p>To detect and address any performance degradation or errors. (B)</p> Signup and view all the answers

Which of the following is NOT a key element of defining business goals for a machine learning project?

<p>Data availability and quality. (D)</p> Signup and view all the answers

Why might converting a business problem into a machine learning problem be challenging?

<p>The business problem may not be well-defined or quantifiable. (B)</p> Signup and view all the answers

What is the primary purpose of feature engineering in a machine learning project?

<p>To create new variables or transform existing ones to improve model accuracy. (A)</p> Signup and view all the answers

Which phase in the machine learning project lifecycle involves ensuring the model is performing as intended and addressing any issues?

<p>Model monitoring. (A)</p> Signup and view all the answers

What is the primary purpose of model development in a machine learning project?

<p>Creating a predictive model using machine learning algorithms. (D)</p> Signup and view all the answers

Why is it important to consider data processing as an iterative process in machine learning?

<p>All of the above. (D)</p> Signup and view all the answers

What is the key difference between a batch deployment model and a real-time deployment model?

<p>Batch models are used for offline predictions, while real-time models are used for online predictions. (C)</p> Signup and view all the answers

What is the primary function of a key performance indicator (KPI) in a machine learning project?

<p>To track the project's progress and ensure it meets business objectives. (A)</p> Signup and view all the answers

What is the primary benefit of conducting exploratory data analysis before model training?

<p>To assess the relevance and quality of the features for the model. (D)</p> Signup and view all the answers

Why is it important to consider the business requirements and context when selecting a deployment model?

<p>To ensure the model's predictions align with the business objectives and user needs. (A)</p> Signup and view all the answers

What is the primary purpose of a correlation matrix in exploratory data analysis?

<p>To visualize the relationships between all pairs of variables. (D)</p> Signup and view all the answers

Why is it important to continuously monitor and iterate on a deployed machine learning model?

<p>To ensure the model continues to provide accurate and relevant predictions over time. (D)</p> Signup and view all the answers

Flashcards

Business Problem Identification

The first step in a machine learning project where a specific business issue is defined and understood.

Framing as ML Problem

Translating the identified business problem into a format suitable for machine learning.

Data Collection

Gathering relevant data to address the machine learning problem identified.

Feature Engineering

The process of transforming raw data into features that better represent the underlying problem to the predictive models.

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Model Training

The phase where a machine learning algorithm learns from the prepared dataset to make predictions.

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Model Evaluation

Assessing the trained model using a test dataset to determine its performance and accuracy.

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Data and Feature Augmentation

Enhancing the model's performance by sourcing additional data or improving existing features.

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Monitoring and Debugging

Regularly checking the model’s performance in real time to ensure predictions are accurate and issues are addressed.

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Machine Learning Loop

A cycle where new data improves model performance through retraining.

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Business Goals

Objectives defined by stakeholders, including value, budget, and success criteria.

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KPI

Key Performance Indicator; a measurable value that demonstrates success.

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Data Preprocessing

Transforming raw data into a clean, usable format for analysis.

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Exploratory Data Analysis (EDA)

Process of analyzing data sets to summarize their main characteristics.

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Correlation Matrix

A table showing correlation coefficients between variables in a data set.

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Model Development

Phase involving training, tuning, and evaluating machine learning models.

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Hyperparameters

Settings that define the structure and training of a machine learning algorithm.

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Model Deployment

Releasing a trained model to make predictions in real-world applications.

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Monitoring Systems

Tools used to track model performance and detect issues post-deployment.

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Retraining

Updating a model using new data to enhance its accuracy.

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Iteration

The process of refining a model based on feedback and new data.

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Deployment Models

Different methods to deploy a model, such as real-time or batch processing.

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Subject Matter Experts

Specialists who provide knowledge in a specific field to guide project decisions.

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Study Notes

AWS AI Practitioner Exam - Machine Learning Project Phases

  • Project Initialization:

    • Identify a solvable business problem
    • Frame it as a machine learning problem
    • Critical to involve stakeholders (value, budget, success criteria, KPIs) to ensure the project aligns with business needs

  • Data Preparation and Exploration:

    • Collect data and convert to usable format (centralized access)
    • Pre-process data (clean, handle missing values)
    • Data visualization to understand data characteristics
    • Exploratory data analysis (EDA): compute stats, visualize with graphs, build correlation matrices for feature correlation understanding
      • Example: Correlation between study hours and test scores (0.85) indicates positive correlation.

  • Feature Engineering:

    • Transform data into features suitable for machine learning models
    • Create, transform, and extract variables
      • Example: Correlation between sleep hours and test scores illustrates relationships for feature selection.

  • Model Development and Evaluation:

    • Model training using prepared dataset
    • Hyperparameter tuning for optimized algorithm performance
    • Evaluate model performance on test data
    • Business goal alignment - if goals are not met, further data enhancements (data augmentation, feature augmentation) and model adjustments are required.

  • Deployment and Monitoring:

    • Deploy trained satisfactory model in a selected deployment method (real-time, batch, serverless, on-premises)
    • Implement monitoring systems to detect and mitigate potential performance or drift issues
    • Continuously monitor model performance in production
      • Crucial to retrain and update models for new data & evolving business needs; example: clothing trends change over time requiring model retraining.

  • Model Retraining and Iteration:

    • Continuously retrain the model using new data to improve accuracy and ensure relevance over time.
    • Iteration is key to improving model performance. Data changes, business needs evolve, so the model must adapt and continually improve.

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