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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 2: Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads |
| Topic 3: Design and implement a GenAIOps infrastructure | - 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 - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 4: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications |
| Topic 5: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question 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_artifact()
B. mlflow.log_batch()
C. mlflow.log_metric()
D. mlflow.log_image()
Question 2
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to register a component in the workspace. The component definition is stored in the local file ./components/train/train.yml.
You write code to connect to the workspace by using the ml_client object and import all required libraries.
You need to complete the remaining 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.
Question 3
Hotspot Question
You are using hyperparameter tuning in Azure Machine Learning Python SDK v2 to train a model.
You configure the hyperparameter tuning experiment by running the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Question 4
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
A. Run a single training job with fixed hyperparameters.
B. Create a tuning job that runs multiple trials with different parameter values.
C. Adjust hyperparameters after model deployment.
D. Export training metrics and compare results outside Azure Machine Learning.
Question 5
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. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Clone an existing Azure Machine Learning workspace to create additional environments.
Does the solution meet the goal?
A. No
B. Yes
Solutions:
| Question 1 Answer: C | Question 2 Answer: Only visible for members | Question 3 Answer: Only visible for members | Question 4 Answer: B | Question 5 Answer: A |
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