Exam Name: Operationalizing Machine Learning and Generative AI Solutions
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?
- A. Delete the model version.
- B. Unregister the model version.
- C. Archive the training dataset for the model version.
- D. Archive the model version.
Correct Answer: D 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
You have an Azure Machine Learning workspace named workspaces.
You must add a datastore that connects an Azure Blob storage container to workspaces. You must be able to configure a privilege level.
You need to configure authentication.
Which authentication method should you use?
- A. SAS token
- B. Managed identity
- C. Account key
- D. Service principal
Correct Answer: B 🗳️
You are designing an Azure Machine Learning solution for traffic optimization.
The model must be deployed as a web service on a serverless compute and provide real-time predictions based on current traffic and weather conditions. You need to choose an inferencing strategy for the solution.
Which compute should you use?
- A. Azure Machine Learning serverless compute
- B. Azure Machine Learning Kubernetes online endpoints
- C. Azure Machine Learning online endpoint
- D. Azure Machine Learning batch endpoint
Correct Answer: C 🗳️
You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script 1 .py.
You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script l.py as part of a training job.
You need to complete the section of script that defines the job parameters.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You manage an Azure Machine learning workspace.
You build a custom model you must log with Mlftow. The custom model includes the following:
* The model is not natively supported by Mlflow.
* The model cannot be serialized in Pickle format.
* The model source code is complex.
* The Python library tor the model must be packaged with the model.
You need to create a custom model flavor to enable logging with ML. flow.
What should you use?
- A. model wrapper
- B. custom signatures
- C. model loader
- D. artifacts
Correct Answer: C 🗳️
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