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Last Updated: Sep 08, 2026
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| Section | Objectives |
|---|---|
| Topic 1: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 3: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 4: Implement secure and scalable AI systems | - Scalability and performance optimization
|
Question 1
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
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 2
Hotspot Question
You manage a Microsoft Foundry project.
You plan to create a search index by using the Microsoft Foundry SDK.
You need to configure the Content field as a prioritized field for semantic ranking. The field is already set to be searchable.
How should you complete the item code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Question 3
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?
A. gpt-4
B. gpt-35-turbo
C. davinci-002
D. gpt-4o
Question 4
Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Question 5
Drag and Drop Question
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Solutions:
| Question 1 Answer: Only visible for members | Question 2 Answer: Only visible for members | Question 3 Answer: D | Question 4 Answer: Only visible for members | Question 5 Answer: Only visible for members |
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