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Microsoft AI-300 Exam Overview:

Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
You must deploy the model to an Azure Machine Learning online endpoint by using the Azure Machine Learning Python SDK v2. The deployed model must return real-time predictions of fraud based on transaction data input.
You need to create the script that is specified as the scoring_script parameter for the CodeConfiguration class used to deploy the model.
What should the entry script do?

A. Load the model and use it to predict labels from input data.
B. Create a Conda environment for the online endpoint compute and install the necessary Python packages.
C. Register the model with appropriate tags and properties.
D. Specify the number of cores and the amount of memory required for the online endpoint compute.
E. Start a node on the inference cluster where the model is deployed.

Question 2

You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?

A. Enable A/B testing and capture live user traffic for data generation.
B. Use a simulator to generate prompt-response interaction data that matches the target task.
C. Run a simulator to produce telemetry logs and trace data from user interactions.
D. Export model evaluation logs and use them directly as training data.

Question 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: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?

A. Yes
B. No

Question 4

Hotspot Question
A team is standardizing MLOps practices by using automated deployments.
The team requires infrastructure to be defined declaratively and deployed through automation pipelines.
You need to configure infrastructure deployment.
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 5

An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?

A. Delete the model version.
B. Unregister the model version.
C. Archive the model version.
D. Archive the training dataset for the model version.

Solutions: