Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 4: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question #1
An organization operates a generative AI application in production by using Microsoft Foundry. The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.
The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.
The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.
You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.
Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Question #2
You need to standardize how Fabrikam Inc. manages machine learning assets.
Which action should you perform first?
A. Deploy a managed online endpoint.
B. Create a shared Azure Machine Learning workspace.
C. Register assets in the Azure Machine Learning registry.
D. Create a new Microsoft Foundry project.
Question #3
You have an Azure Machine Learning (ML) model deployed to an online endpoint.
You need to review container logs from the endpoint by using Azure Ml Python SDK v2. The logs must include the console log from the inference server with print/log statements from the models scoring script.
What should you do first?
A. Create an instance of the the MLCIient class.
B. Connect by using Docker tools to the inference server.
C. Connect by using SSH to the inference server.
D. Create an instance of the OnlineDeploymentOperations class.
Question #4
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files. What should you do?
A. Move the file named. gift ignore to the. /logs directory.
B. Move the file named. gift ignore to the. /outputs directory.
C. Copy the contents of the file named. gift ignore to the file named. amlignore.
D. Add the contents of the file named. amlignore to the file named. gift ignore.
Question #5
You manage an Azure Machine Learning workspace named Workspace1.
You plan to create a pipeline in the Azure Machine Learning Studio designer. The pipeline must include a custom component You need to ensure the custom component can be used in the pipeline. What should you do first.
A. Add a linked service to Workspace1.
B. Create a pipeline endpoint.
C. Upload a json file to Workspace1.
D. Upload a yaml file to Workspace1.
E. Create a datastore.
Solutions:
| Question #1 Correct Answer: Only visible for members | Question #2 Correct Answer: B | Question #3 Correct Answer: A | Question #4 Correct Answer: C | Question #5 Correct Answer: A |
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