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Updated: Oct 23, 2026
No. of Questions: 189 Questions & Answers with Testing Engine
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Microsoft AI-300 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Operationalizing Machine Learning and Generative AI Solutions |
| Exam Number: | AI-300 |
| Real Exam Qty: | 40–60 |
| Exam Price: | 165 USD |
| Exam Format: | Multiple choice, Case study, Scenario-based, Performance-based items |
| Passing Score: | 700 |
| Available Languages: | French, Japanese, Chinese (Simplified), English, German, Korean, Portuguese (Brazil), Spanish |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Data Scientist Associate |
| Exam Duration: | 100–120 |
| Certificate Validity Period: | 1 year |
| Recommended Training: | Microsoft Learn: Operationalizing Machine Learning and Generative AI Solutions |
| Exam Registration: | Pearson VUE Scheduling Microsoft Learn Registration |
| Sample Questions: | Microsoft AI-300 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Recommended: Experience with Azure Machine Learning, Microsoft Foundry, Python, DevOps practices, and infrastructure as code; no mandatory prerequisites |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-300 |
Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
The team needs to introduce a new version of a model to production without disrupting existing users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?
- A. Deploy the model to a batch endpoint.
- B. Route all traffic to the new deployment.
- C. Replace the existing endpoint.
- D. Split traffic between deployments.
Correct Answer: D 🗳️
Explanation: Only visible for ActualTestsQuiz members. You can sign-up / login (it's free).
You create an Azure Machine Learning workspace named workspaces. You create a Python SDK v2 notebook to perform custom model training in workspace1. You need to run the notebook from Azure Machine Learning Studio in workspace1. What should you provision first?
- A. default storage account
- B. Azure Machine Learning compute cluster
- C. real-time endpoint
- D. Azure Machine Learning compute instance
Correct Answer: D 🗳️
-
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2- based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Termination policy type: Bandit
Termination policy parameter: slack_factor
The correct early termination policy is Bandit because the requirement explicitly compares each training run against the best-performing run . Microsoft defines BanditPolicy as an early termination mechanism that stops runs that fall outside a permitted performance margin relative to the best run observed so far.
The correct parameter is slack_factor because the threshold is expressed as a percentage , not as an absolute difference in the metric. Microsoft defines slack_factor as the ratio representing the allowable distance from the best-performing run. Therefore, a 25% tolerance would be configured conceptually as:
slack_factor = 0.25
A trial whose primary metric falls outside that permitted 25% band is eligible for early termination at the configured evaluation interval. Microsoft provides the same pattern in SDK v2 examples, such as BanditPolicy (slack_factor=0.15, ...) for a 15% relative tolerance.
Median stopping compares runs with the median performance across completed or running trials rather than strictly against the current best run. Truncation selection terminates a specified percentage of the worst- performing trials, so neither matches this requirement.
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.)
- A. Fine-tune the model for each conversational variation.
- B. Repeat the instructions at the end of the system prompt.
- C. Increase the temperature setting to encourage creativity.
- D. Use the system prompt to establish the role, tone, and style.
- E. Apply prompt transformations to separate system instructions from user input.
Correct Answer: D,E 🗳️
Explanation: Only visible for ActualTestsQuiz members. You can sign-up / login (it's free).
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
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