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Updated: Oct 03, 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 |
| Passing Score: | 700 |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Data Scientist Associate |
| Real Exam Qty: | 40–60 |
| Exam Duration: | 100–120 |
| Exam Format: | Multiple choice, Performance-based items, Case study, Scenario-based |
| Certificate Validity Period: | 1 year |
| Available Languages: | Japanese, English, Chinese (Simplified), French, Spanish, Portuguese (Brazil), German, Korean |
| Exam Price: | 165 USD |
| Recommended Training: | Microsoft Learn: Operationalizing Machine Learning and Generative AI Solutions |
| Exam Registration: | Microsoft Learn Registration Pearson VUE Scheduling |
| 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 |
|---|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Topic 2: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Topic 3: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 4: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?
- A. fairlearn.preprocessing.CorrelationRemover
- B. fairlearn.reductions.GridSearch
- C. fairlearn.postprocessing.ThresholdOptimizer
- D. fairiearn.reductions.ExponentiatedGradient
Correct Answer: C 🗳️
Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).
You manage an Azure Machine Learning workspace.
An MLflow model is already registered. You plan to customize how the deployment does inference. You need to deploy the MLflow model to a batch endpoint for batch inferencing. What should you create first?
- A. deployment definition
- B. scoring script
- C. deployment
- D. environment
Correct Answer: B 🗳️
-
A team is developing a Retrieval-Augmented Generation (RAG) system.
The team requires improvements to the system ' s retrieval quality to ensure accurate, grounded responses.
You need to assess RAG performance before you can suggest an improvement strategy.
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:
Explanation:
Correct sequence:
* Collect retrieval logs.
* Run RAG evaluators.
* Adjust chunking strategy.
* Re-index documents.
The process should begin by collecting retrieval logs . Retrieval diagnostics provide the evidence needed to understand which queries were issued, which document chunks were returned, and whether relevant content is being surfaced. Without retrieval telemetry, optimization becomes speculative rather than measurable.
Next, run RAG evaluators . Microsoft Foundry provides retrieval-oriented evaluators that assess how relevant retrieved context is to a query. The Retrieval evaluator measures contextual relevance without requiring ground truth, while the Document Retrieval evaluator can calculate metrics such as Fidelity and NDCG when labeled retrieval ground truth exists. These measurements help establish the retrieval-quality baseline before modifications are made.
After identifying retrieval deficiencies, adjust the chunking strategy . Microsoft specifically recommends reviewing chunk size and chunking methodology when retrieval returns irrelevant or incomplete passages.
Chunks that are too small can lose context, while oversized chunks can introduce irrelevant material and reduce retrieval precision.
Finally, re-index the documents so the revised chunking configuration is reflected in the searchable corpus.
Changing temperature or regenerating the prompt template primarily affects generation behavior rather than correcting the underlying retrieval pipeline.
Study Guide Reference: Implement generative AI quality assurance and observability - RAG evaluation, retrieval telemetry, retrieval-quality metrics, chunk optimization, indexing, and grounded-response assessment.
Fabrikam Inc. must improve its deployment process because traditional machine learning models are deployed manually and the organization has limited rollback capability .
You need to recommend a deployment approach that supports staged rollout and rollback while minimizing operational overhead.
Which deployment approach should you recommend?
- A. Azure Kubernetes Service with blue-green switching
- B. Batch endpoints
- C. Managed online endpoints with traffic splitting
- D. VM-hosted REST APIs
Correct Answer: C 🗳️
Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
* Accounts for the performance of all previous runs when evaluating the current run.
* Avoids comparing the current run with only the best performing run to date.
Which two early termination policies should you use? Each correct answer presents part of the solution.
- A. Bandit
- B. Median stopping
- C. Default
- D. Truncation selection
Correct Answer: B,D 🗳️
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