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The AI-300 certificate won't chase you; you have to chase it. ITExamDownload shortens the chase with 189 practice questions for Microsoft Operationalizing Machine Learning and Generative AI Solutions — expert-written, exam-shaped, and ready the minute you are.
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Operationalizing Machine Learning and Generative AI Solutions |
| Exam Number: | AI-300 |
| Related Certifications: | Machine Learning Operations (MLOps) Engineer Associate |
| Exam Duration: | 100-120 |
| Available Languages: | Italian, Chinese (Simplified), English, Russian, Arabic (Saudi Arabia), Chinese (Traditional), Portuguese (Brazil), French, Japanese, Spanish, Indonesian (Indonesia), Korean, German |
| Exam Price: | $165 USD |
| Exam Format: | Multiple choice, Drag and drop, Multiple response, Case study, Build list |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | 1 year (renewable) |
| Real Exam Qty: | 40-60 |
| Sample Questions: | ![]() |
| Exam Way: | Online (proctored via Pearson VUE) or at a Pearson VUE testing center |
| Pre Condition: | Candidates should have subject matter expertise in setting up infrastructure for MLOps and GenAIOps solutions on Azure, with experience in training, deploying, and maintaining ML models using Azure Machine Learning and generative AI applications using Microsoft Foundry. No formal prerequisite exam is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-300 |
| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries |
| Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput |
| Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding |
| Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
The AI-300 exam is Microsoft's route to the Microsoft Certified certification, a credential at the Associate level. Earning it tells the job market you can apply the Microsoft Operationalizing Machine Learning and Generative AI Solutions objectives to real work — the kind of proof that moves resumes to the top of the pile. It also connects to a wider certification family covering Machine Learning Operations (MLOps) Engineer Associate, so the investment compounds. Wanting it is easy; structured practice is what closes the gap.
The numbers in the overview table above become a plan once you divide them: total time over total questions equals your per-item budget, and any question burning double its budget should be flagged and revisited at the end. Rehearse that discipline against a live timer — every ITExamDownload engine session enforces one — until finishing with spare review time feels routine rather than lucky.
Every retake bills the full fee again, so the passing score above is really a budgeting problem. The financially sound approach: drill with ITExamDownload practice questions until your timed scores clear the mark across several consecutive sessions, and only then register. Over-preparation costs hours; re-registration costs money.
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The blueprint above spans 5 domains — read the weightings as your study budget. A domain carrying of the score deserves a matching share of your week, while lighter ones like Optimize generative AI systems and model performance can be maintained rather than marathoned. Work the full topic list above domain by domain, and let the percentages decide where your practice time goes first.
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Work in passes rather than one grind. First pass: move through the 189 questions untimed, mapping weak domains against the blueprint above. Second pass: timed engine sessions, reviewing every error the same day while the reasoning is fresh. Final pass: re-test only your flagged questions until they stop being wrong. Book the exam when your timed scores hold steady, not when your calendar gets impatient.
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.
Which file format should you use?
Correct Answer: C 🗳️
Explanation: Only visible for ITExamDownload members. You can sign-up / login (it's free).
You have an Azure subscription that contains a resource group named rg-ml.
You plan to create an Azure Machine Learning workspace named workspacel in rg-ml by using Azure Machine Learning Python SDK v2.
You need to ensure workspacel is configured to prevent the collection of potentially sensitive data by Microsoft diagnostics.
How should you complete the provided code? To answer, select the appropnate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:

Explanation:
An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?
Correct Answer: C 🗳️
Explanation: Only visible for ITExamDownload 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.
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?
Correct Answer: A 🗳️
Explanation: Only visible for ITExamDownload members. You can sign-up / login (it's free).
You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script 1 .py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script l.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:

Explanation:
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