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Microsoft AI-300 Exam Syllabus Topics:

Section	Objectives
Design and implement an MLOps infrastructure	- Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows
Design and implement a GenAIOps infrastructure	- Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search
Optimize generative AI systems and model performance	- Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput
Implement generative AI quality assurance and observability	- Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding
Implement machine learning model lifecycle and operations	- Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. 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.

2. A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

A) Notebook-based retraining process
B) Azure Function to retrain model manually
C) One pipeline triggered by schedule and data drift alerts
D) Two independent pipelines with shared scripts

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. Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named model_train.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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 in the review screen.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.6 - AzureML kernel.
Does the solution meet the goal?

A) No
B) Yes

Solutions:

Question # 1
Answer: Only visible for members

Question # 2
Answer: C

Question # 3
Answer: Only visible for members

Question # 4
Answer: Only visible for members

Question # 5
Answer: A