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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. 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?
A) JSON
B) TSV
C) CSV
D) JSONL
2. Drag and Drop Question
A data science team plans to evaluate multiple algorithms and hyperparameters for a classification problem without manually authoring separate training scripts.
The team must run an automated machine learning (AutoML) job that compares models and identifies the best-performing configuration.
You need to configure and run an AutoML training job.
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.
3. Drag and Drop Question
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
Which three 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.
4. You must ensure full reproducibility of experiments including dataset, code, and environment across multiple runs and workspaces. Which combination of practices is MOST appropriate?
A) Git only
B) Environment + dataset + code versioning
C) Logging metrics only
D) Dataset versioning only
5. Hotspot Question
A team is standardizing MLOps practices by using automated deployments.
The team requires infrastructure to be defined declaratively and deployed through automation pipelines.
You need to configure infrastructure deployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: Only visible for members | Question # 3 Answer: Only visible for members | Question # 4 Answer: B | Question # 5 Answer: Only visible for members |
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