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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team's organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?

A) Data Zone Batch
B) Developer
C) Global Batch
D) Data Zone Standard

2. You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?

A) Use prompt flow to generate multiple prompt templates for evaluation.
B) Format the dataset as a JSONL file with prompt-completion pairs and upload the file.
C) Enable tracing for all inference calls in the evaluation pipeline.
D) Deploy the base model to an online endpoint before starting fine-tuning.

3. Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?

A) Blob storage containers
B) Data lake
C) Git repositories
D) Model registry

4. Drag and Drop Question
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
- Stakeholders must be notified of the drops.
- Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
Which four 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.

5. A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution.
(Choose two.)
NOTE: Each correct selection is worth one point.

A) Use the system prompt to establish the role, tone, and style.
B) Increase the temperature setting to encourage creativity.
C) Apply prompt transformations to separate system instructions from user input.
D) Repeat the instructions at the end of the system prompt.
E) Fine-tune the model for each conversational variation.

Solutions: