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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?

A) image
B) conda_file
C) properties
D) build

2. You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?

A) mlflow.log_artifact()
B) mlflow.log_batch()
C) mlflow.log_image()
D) mlflow.log_metric()

3. 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: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?

A) No
B) Yes

4. A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?

A) Chunk size and overlap
B) Temperature parameter
C) Token limits
D) Embedding strategy

5. Drag and Drop Question
A team validates a generative AI application that produces free-form text responses by using Microsoft Foundry SDK.
The evaluation dataset is registered in the Microsoft Foundry environment.
You need to configure a safety evaluation pipeline that reliably evaluates model outputs for harmful content.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Solutions: