Microsoft AI-300 : Operationalizing Machine Learning and Generative AI Solutions

AI-300 pass collection

Exam Code: AI-300

Exam Name: Operationalizing Machine Learning and Generative AI Solutions

Updated: Jun 05, 2026

Q & A: 77 Questions and Answers

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?

A) ISO-8859-1
B) ASCII
C) UTF-8
D) UTF-16


2. A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

A) Two independent pipelines with shared scripts
B) Notebook-based retraining process
C) Azure Function to retrain model manually
D) One pipeline triggered by schedule and data drift alerts


3. 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) Global Batch
B) Developer
C) Data Zone Batch
D) Data Zone Standard


4. Drag and Drop Question
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.


5. A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?

A) Textual similarity
B) Coherence
C) Fluency
D) Grounded ness


Solutions:

Question # 1
Answer: C
Question # 2
Answer: D
Question # 3
Answer: D
Question # 4
Answer: Only visible for members
Question # 5
Answer: C

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