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Updated: Aug 12, 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 |
| Exam Duration: | Not officially published / TBD |
| Exam Format: | Multiple choice, Multiple response, Case studies, Scenario-based questions |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure Solutions Architect Expert |
| Available Languages: | English |
| Passing Score: | Not officially published / TBD |
| Real Exam Qty: | Not officially published / TBD |
| Exam Price: | Varies by region (typically ~USD 165, subject to Microsoft regional pricing) |
| Certificate Validity Period: | Typically 1 year (renewable, depending on Microsoft certification policy) |
| Recommended Training: | Microsoft Learn Azure AI Engineer learning paths Azure OpenAI and Generative AI learning modules |
| Exam Registration: | Pearson VUE Microsoft Exams Microsoft Learn Certification Portal |
| 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 |
|---|---|
| Topic 1: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 2: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 3: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 4: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. 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.
2. You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
A) Deploy the base model to an online endpoint before starting fine-tuning.
B) Enable tracing for all inference calls in the evaluation pipeline.
C) Use prompt flow to generate multiple prompt templates for evaluation.
D) Format the dataset as a JSONL file with prompt-completion pairs and upload the file.
3. Hotspot Question
A team is preparing a generative AI application for production deployment. The application generates structured responses that must be evaluated for quality before each release.
The organization requires repeatable evaluation results that can be compared across builds and environments.
You need to configure evaluation inputs so quality metrics can be reliably calculated across test runs.
How should you prepare the evaluation inputs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
4. You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?
A) Define and reuse environment configurations
B) Use batch endpoints
C) Increase endpoint compute size
D) Use same compute for training and inference
5. During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?
A) Use larger compute
B) Retrain manually when failure occurs
C) Add a data validation component in pipeline
D) Ignore schema differences
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: Only visible for members | Question # 4 Answer: A | Question # 5 Answer: C |
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