AI-300 Practice Test

AI-300 Practice Test practice questions, page 7 of 7, with answer review and explanations.

Question 31

A team manages an Azure Machine Learning workspace and deploys a model to an endpoint. A deployed online endpoint shows inconsistent response times during periods of high traffic. You need to identify potential performance degradation. Which three metrics should you monitor? Each correct answer presents part of the solution. Choose three. NOTE: Each correct selection is worth one point.

  1. Feature count
  2. Requests per minute
  3. Connections active
  4. Dataset size
  5. Request latency

Question 32

A team deploys a machine learning model to a managed online endpoint. The team monitors model performance and data quality metrics in production. When monitoring thresholds are exceeded, the team requires an automated operational response that notifies downstream systems. You need to configure the monitoring solution to meet the requirements. Which configuration should you associate with each requirement as a first step? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

Question 33

You use Azure Machine Learning to train models across multiple experiments by using the same workspace. You must record training runs in a centralized location to compare results from different jobs. During training, performance values must be captured so they appear in the experiment run history. You need to configure experiment tracking. What should you configure for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 34

A data science team trains a classification model that predicts loan approval outcomes. Before registering the model, the team must ensure the following: Predictions must not disproportionately impact protected groups. Prediction errors can be evaluated across different data segments. You need to assess whether the model meets Responsible AI expectations. Which two approaches should you use? Each correct answer presents part of the solution. Choose two. NOTE: Each correct selection is worth one point.

  1. Analyze error rates across the global cohort.
  2. Measure endpoint latency under load.
  3. Validate inference schema compatibility.
  4. Evaluate feature importance for prediction transparency.
  5. Analyze error rates across defined demographic cohorts.