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An Azure Machine Learning workspace processes sensitive training data. The workspace must NOT be accessible from the public internet. You need to restrict network access. Which configuration should you implement?
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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: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data. Does the solution meet the goal?
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HOTSPOT - 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
Answer is in the explanation below.
