Machine Learning Operations (MLOps) Engineer Associate Certification (AI-300) - Microsoft Exam Questions

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen. You work in Microsoft Foundry with a prompt flow. You must manually evaluate prompts and compare results across prompt variants. You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation. Solution: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data. Does the solution meet the goal?

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

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE:
introducing regression. Each correct selection is worth one point.

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. NOTE: Each correct selection is worth one point. Choose two.

You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base. You need to select an evaluation metric.

Which built-in evaluator should you use?

A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning. The team requires a safe way to validate a new model version without disrupting existing users. You need to recommend a deployment strategy for controlled testing of a new model version.

What should you configure?