Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified
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Updated: Oct 29, 2026
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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 |
| Exam Price: | $165 USD |
| Exam Duration: | 100-120 |
| Available Languages: | Italian, Portuguese (Brazil), Japanese, Russian, French, Korean, Chinese (Traditional), Arabic (Saudi Arabia), Chinese (Simplified), Indonesian (Indonesia), Spanish, German, English |
| Exam Format: | Build list, Drag and drop, Multiple choice, Case study, Multiple response |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Machine Learning Operations (MLOps) Engineer Associate |
| Certificate Validity Period: | 1 year (renewable) |
| Passing Score: | 700/1000 |
| Sample Questions: | DOWNLOAD DEMO |
| 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 |
Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
| Topic 2: Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows - Implement security, governance, and compliance for MLOps |
| Topic 3: Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| Topic 4: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks |
| Topic 5: 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 |
What to Know Before Your AI-300 Exam Day
The procedure is easy and time-saving. As soon as you pay, you receive an email with the complete AI-300 file attached — typically within about a minute — plus an instant download link, so you can download immediately and devote yourself to studying. If nothing arrives within two hours, check your spam folder and contact support. Store the materials on your phone, pad, or computer and install without limits. Updates are free for 365 days, emailed automatically on release, with a 50% renewal discount afterward.
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The latest exam information lists 40-60 questions for the AI-300 exam within a 100-120-minute window. Practicing under those exact conditions is the best cure for exam-day surprises.
The Microsoft Operationalizing Machine Learning and Generative AI Solutions blueprint is organized around these core domains:
- Implement machine learning model lifecycle and operations
- Design and implement a GenAIOps infrastructure
- Design and implement an MLOps infrastructure
Further domains appear in the full official outline; the question bank covers all of them.
By fitting study into the cracks of a busy life. The AI-300 bank contains complete questions with expert-verified answers across the Microsoft Operationalizing Machine Learning and Generative AI Solutions objectives, stored easily on your phone, pad, or computer for spare-time review — and the PDF prints cleanly when you prefer paper, because marking key points by hand genuinely enhances memory. Assess everything first through the free demo on our site, pay securely through the Credit Card system, and rest assured your personal information is never shared with third parties without permission.
Microsoft specifies these prerequisites for the Microsoft Operationalizing Machine Learning and Generative AI Solutions: 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..
Check the authoritative wording on the official certification page before booking.
At present, the AI-300 exam requires a passing score of 700/1000, and registration costs $165 USD. Microsoft sets both figures and may adjust them, so verify on the official site before you schedule.
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You manage an Azure Machine Learning workspace that includes a batch endpoint. You plan to deploy a model to the batch endpoint. You need to configure compute for the deployment. Which compute should you use?
- A. Azure Databricks
- B. Kubernetes cluster
- C. Remote VM
- D. Azure Batch
Correct Answer: A 🗳️
Fabrikam Inc. needs to improve the performance of a GPT-5 model based on the stated technical requirements.
Which action should you perform first?
- A. Generate synthetic interaction data.
- B. Deploy the model to production to gather real-world feedback.
- C. Evaluate the model output.
- D. Fine-tune the model to improve accuracy.
Correct Answer: C 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
You retrain an existing model.
You need to register the new version of a model while keeping the current version of the model in the registry.
What should you do?
- A. Register the model with the same name as the existing model.
- B. Save the new model in the default datastore with the same name as the existing model. Do not register the new model.
- C. Register a model with a different name from the existing model and a custom property named version with the value 2.
- D. Delete the existing model and register the new one with the same name.
Correct Answer: A 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage1. The storage account includes a publicly accessible container named mlcontainer1. The container stores 10 blobs with files in the CSV format.
You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer1.
You need to complete the Python SDK v2 code.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
Correct Answer:
Explanation:
Because you want to reference all blobs in a container rather than a single file, the correct asset type is AssetTypes.URI_FOLDER. This type points to a directory-level URI, allowing Azure ML to traverse all files within it. For a publicly accessible Azure Blob Storage container, the URI follows the pattern
https://storage_account.blob.core.windows.net/container_name/. You then create the Data object with the correct type, path, name, and version parameters, and register it in the workspace using ml_client.data.
create_or_update. Do not use AssetTypes.URI_FILE, which references a single file. Do not use AssetTypes.
MLTABLE unless you have an MLTable YAML descriptor. URI_FOLDER is the correct choice for referencing a collection of CSV blobs in an Azure Blob Storage container, allowing Azure ML to discover and process all files within the specified path.
Microsoft Learn Reference Topic: Create and manage data assets in Azure Machine Learning Python SDK v2
- URI_FOLDER
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?
- A. Yes
- B. No
Correct Answer: B 🗳️
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