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| Section | Objectives |
|---|---|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Implement secure and scalable AI systems | - Security and governance
|
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NEW QUESTION # 50
You manage an Azure Machine Learning workspace.
You must create and configure a compute cluster for a training job by using Python SDK v2.
You need to create a persistent Azure Machine Learning compute resource, specifying the fewest possible properties.
Which two properties should you define? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
NEW QUESTION # 51
You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and model artifacts automatically when training a model.
You start by writing the following code segment:
For each of the following statements, select Yes If the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
NEW QUESTION # 52
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.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?
Answer: B
Explanation:
Enabling tracing via the prompt flow SDK alone does not guarantee that all four required dimensions - inputs, outputs, token usage, and latencies - are captured and surfaced in the Microsoft Foundry portal ' s trace viewer in a way that allows manual comparison across runs. While the prompt flow SDK does support tracing configuration, the portal-based Tracing activation is the complete, supported path for this specific capture requirement. The correct and complete solution as confirmed by the next question in this series is to use the Tracing toggle directly in the Microsoft Foundry portal for the project, then execute test runs. This portal-side Tracing feature is purpose-built to instrument all four dimensions and store them in a queryable trace store accessible through the Foundry interface.
Microsoft Learn Reference Topic: Enable tracing for prompt flows in Microsoft Foundry portal - Capturing run telemetry
NEW QUESTION # 53
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
How should you complete the code?
Box 1: container_name
container_name specifies the parameter name used in the AzureBlobDatastore constructor to identify your target blob storage container.
Box 2: wasbs
Correct Code Formats
Depending on your preference for the storage connection protocol, your completed line of code should look like one of the following variations:
Using the standard HTTPS protocol (Default).
-> Using the WASBS (Windows Azure Storage Blob Secure).
Reference:
https://stackoverflow.com/questions/75275875/create-an-azureblobdatastore-with-sdk-v2
NEW QUESTION # 54
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Answer: C
Explanation:
Azure OpenAI Service fine-tuning requires training data in JSON Lines (JSONL) format - a text file where each line is a self-contained, valid JSON object representing one training example. For chat fine-tuning, each line contains a messages array with system, user, and assistant turns. JSONL is ideal for machine-learning datasets because it is streamable, easy to generate programmatically, and directly supported by the Azure OpenAI fine-tuning API. CSV (option A) and TSV (option B) are tabular formats that cannot natively represent the nested, multi-turn conversation structure required by chat models. Plain JSON (option D) would require loading the entire file as a single object, which is impractical for large datasets and is not accepted by the Azure OpenAI fine-tuning endpoint. Always use JSONL with UTF-8 encoding for Azure OpenAI training files.
Microsoft Learn Reference Topic: Prepare training data for Azure OpenAI fine-tuning - JSONL file format
NEW QUESTION # 55
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