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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails |
| 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 machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production |
| Topic 4: Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications |
| Topic 5: Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?
A) mlflow.log_metric()
B) mlflow.log_batch()
C) mlflow.log_image()
D) mlflow.log_artifact()
2. Hotspot Question
You manage a Microsoft Foundry project.
You plan to build a RAG solution.
The solution must include two models:
- One for text output, named Model1. This model must resemble human
language and read naturally.
- One for creating embeddings, named Model2. This model must maximize
the retrieval of relevant results (high recall) while minimizing
irrelevant or incorrect matches (high precision).
You need to compare different models by using benchmarking metrics to select the appropriate models for Model1 and Model2.
Which benchmarking metric should you select for each model? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
3. Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
4. You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?
A) Same dataset name
B) Same compute SKU
C) Same user identity
D) Versioned datasets and environments
5. Hotspot Question
You manage a Microsoft Foundry project.
You are developing a solution to generate content based on text and images. The solution requires the ability to manage high-volume processing and avoid disruptions to the online workloads.
You need to deploy the solution.
Which deployment type and large language model (LLM) should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: Only visible for members | Question # 3 Answer: Only visible for members | Question # 4 Answer: D | Question # 5 Answer: Only visible for members |
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