Learn with Microsoft : AI-300 training material for 100% pass
Last Updated: Sep 12, 2026
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 2: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 3: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 4: Design and implement generative AI solutions | - Large language model integration
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Drag and Drop Question
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
- A. Run a single training job with fixed hyperparameters.
- B. Create a tuning job that runs multiple trials with different parameter values.
- C. Adjust hyperparameters after model deployment.
- D. Select hyperparameters based only on default model settings.
Correct Answer: B 🗳️
Explanation: Only visible for Exam4Labs members. You can sign-up / login (it's free).
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 in 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: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
Explanation: Only visible for Exam4Labs members. You can sign-up / login (it's free).
Drag and Drop Question
A team deploys a generative AI application that uses a model deployed in Microsoft Foundry. The application must support latency monitoring under production load.
You need to enable performance observability.
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.
Correct Answer:
Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
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