AI-300 Free Practice Questions — Page 6

Question 22

DRAG DROP A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application. Users report intermittent failures and unexpected responses when calling the endpoint. You need to identify the appropriate troubleshooting action for each reported issue. Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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 23

DRAG DROP - An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads. The organization has the following workload requirements: • One workload must be real-time, latency-sensitive, and have predictable global usage patterns that demand consistent performance. • One workload must have variable performance and be optimized for cost-efficient operation. You need to select a global deployment type for each workload. Which type of deployment should you use for each workload requirement? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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 23

HOTSPOT You are monitoring a fine-tuned large language model deployed in Microsoft Foundry. You evaluate the model before and after fine-tuning by using the same evaluation dataset. You review the following evaluation results: You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Question 24

A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning. The tuning process must run multiple training trials without manually modifying the training script for each run. You need to automate hyperparameter tuning for the training job. What should you do?

Question 24

DRAG DROP - 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 25

You are authoring a notebook in Azure Machine Learning studio. You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only. You need to install the packages. Which magic function should you use?

Question 25

You create a binary classification model. You use the Fairlearn package to assess model fairness. You must eliminate the need to retrain the model. You need to implement the Fairlearn package. Which algorithm should you use?

Question 26

HOTSPOT - A biomedical research company plans to enroll people in an experimental medical treatment trial. You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity. The model returns different performance metrics for people from different ethnic groups. You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature. Which technique and constraint should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 26

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.

Question 27

HOTSPOT - 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.