Prepare with Microsoft AI-300 exam practice material, pass for sure
Updated: Sep 13, 2026
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Microsoft AI-300 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Operationalizing Machine Learning and Generative AI Solutions |
| Exam Number: | AI-300 |
| Certificate Validity Period: | Typically 1 year (renewable, depending on Microsoft certification policy) |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure Solutions Architect Expert |
| Real Exam Qty: | Not officially published / TBD |
| Available Languages: | English |
| Exam Price: | Varies by region (typically ~USD 165, subject to Microsoft regional pricing) |
| Exam Format: | Scenario-based questions, Multiple choice, Multiple response, Case studies |
| Passing Score: | Not officially published / TBD |
| Exam Duration: | Not officially published / TBD |
| Recommended Training: | Microsoft Learn Azure AI Engineer learning paths Azure OpenAI and Generative AI learning modules |
| Exam Registration: | Microsoft Learn Certification Portal Pearson VUE Microsoft Exams |
| Sample Questions: | Microsoft AI-300 Sample Questions |
| Exam Way: | Online proctored or authorized testing center (Pearson VUE, depending on region) |
| Pre Condition: | No formal prerequisite required, but recommended experience with Azure AI services, machine learning concepts, and basic cloud architecture knowledge. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/ |
Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Security and governance
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Create an environment.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: B 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
Drag and Drop Question
A data science team plans to evaluate multiple algorithms and hyperparameters for a classification problem without manually authoring separate training scripts.
The team must run an automated machine learning (AutoML) job that compares models and identifies the best-performing configuration.
You need to configure and run an AutoML training job.
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.
Correct Answer:
A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.
The team requires a safe way to validate a new model version without disrupting existing users.
You need to recommend a deployment strategy for controlled testing of a new model version.
What should you configure?
- A. an evaluation script in Azure Machine Learning
- B. the model asset version in the registry
- C. deployment to a separate staging endpoint
- D. traffic splitting between deployments
Correct Answer: D 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
A team manages prompts used by a generative AI application built on Microsoft Foundry. Prompt updates are frequent, and prior changes have caused unexpected behavior in production.
The team requires that new prompt versions can be tested and reviewed without affecting production behavior.
You need to implement a source control practice that allows safe prompt experimentation.
What should you do?
- A. Create a feature git branch for each prompt change.
- B. Revert the prompt in the production environment to a previous commit.
- C. Create a GitHub Action workflow.
Correct Answer: A 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.
- A. Requests per minute
- B. Connections active
- C. Dataset size
- D. Request latency
- E. Feature count
Correct Answer: A,B,D 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
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