Microsoft Azure AI Fundamentals Artificial Intelligence 3 — Questions and Answers
Question 1: Which concept describes the ability of an AI system to explain how it reached a particular decision or prediction?
- Scalability
- Interpretability (Correct answer)
- Redundancy
- Normalization
Correct answer: Interpretability
Interpretability (also called explainability) refers to how understandable the reasoning behind an AI model's output is to humans.
Question 2: A retailer wants to predict next month's sales figures based on historical data. Which type of machine learning task is this?
- Classification
- Clustering
- Regression (Correct answer)
- Anomaly detection
Correct answer: Regression
Regression predicts a continuous numerical value, such as a sales figure, based on input features.
Question 3: What is the role of features in a machine learning model?
- They are the output labels the model predicts
- They are the input variables used to make predictions (Correct answer)
- They are the algorithms used to train the model
- They are the evaluation metrics for model performance
Correct answer: They are the input variables used to make predictions
Features are the measurable input variables (columns in a dataset) that a model uses to make predictions.
Question 4: Which responsible AI principle ensures that AI systems do not disadvantage particular groups of people unfairly?
- Privacy and security
- Inclusiveness
- Fairness (Correct answer)
- Accountability
Correct answer: Fairness
The fairness principle requires that AI systems treat all people equitably and do not create or reinforce biased outcomes.
Question 5: In machine learning, what does 'training' a model refer to?
- Deploying the model to a production endpoint
- Adjusting model parameters to minimize error on a dataset (Correct answer)
- Monitoring model performance after deployment
- Collecting and labeling new data samples
Correct answer: Adjusting model parameters to minimize error on a dataset
Training is the process of iteratively adjusting a model's internal parameters to minimize prediction error on the training dataset.
Question 6: Which metric is most appropriate for evaluating a binary classification model when the dataset is highly imbalanced?
- Accuracy
- Mean Absolute Error
- F1 Score (Correct answer)
- R-squared
Correct answer: F1 Score
F1 Score balances precision and recall, making it more informative than accuracy when one class is much more common than the other.
Question 7: What is the purpose of the Azure AI Foundry (formerly Azure AI Studio)?
- To manage virtual machine fleets for AI compute
- To provide a unified platform for building, evaluating, and deploying generative AI applications (Correct answer)
- To monitor network traffic for AI workloads
- To automate database migrations for AI applications
Correct answer: To provide a unified platform for building, evaluating, and deploying generative AI applications
Azure AI Foundry is Microsoft's platform for developing, evaluating, and deploying enterprise-grade generative AI solutions and agents.
Microsoft Azure AI Fundamentals Artificial Intelligence 3
Which concept describes the ability of an AI system to explain how it reached a particular decision or prediction?