Hotspot Question
A team retrains a machine learning model on a weekly basis by using updated training data.
The team must be able to rerun any previous experiment by using the exact data that was available at the time it was originally run. The solution must preserve historical versions of the data without duplicating training scripts.
You need to manage the data so that experiments can be reproduced reliably.
Which workspace actions should you perform? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Box 1: Create a versioned dataset
Track data changes over time.
Creating a versioned dataset in your machine learning workspace configuration is the correct and industry-standard way to solve this.
Versioned datasets allow you to decouple your training scripts from your data state. This approach satisfies all your constraints without duplicating code or creating messy infrastructure.
Enables Reproducibility: Every time you run a training job, you log the specific dataset version used (e.g., v1, v2, v3). To rerun a historical experiment, you simply call that specific version identifier.
Prevents Code Duplication: Your training scripts remain identical. They accept a dataset version as an input parameter rather than pointing to hardcoded file paths.
Tracks Weekly Updates: When the weekly data refresh occurs, you register the new data as v+1 in the workspace registry, keeping a clean chronological ledger.
Box 2: Use the data asset in the experiment
Associate a specific dataset version with a run.
To best associate a specific dataset version with a run in the workspace, you should use the data asset in the experiment.
Automatic Lineage: Registering data as a versioned data asset automatically tracks which exact version was used in which specific run.
Exact Reproducibility: You can look back at any historical run and instantly identify or retrieve the precise data state used at that moment.
No Script Duplication: Your training scripts remain decoupled from the data, satisfying your requirement to preserve history without changing code.
Incorrect:
[Pass the storage key at runtime]
Passing a storage key at runtime only connects the run to a storage location, not a specific, immutable version of the data at a frozen point in time.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-version-track-datasets

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A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates.
The team requires a centralized way to track, version, and reuse prompt content across projects.
You need to recommend a solution to track and reuse prompt content.
Which approach should you recommend?

• A.Store prompts as versioned files in a Git repository.
• B.Register prompts as datasets in the Azure Machine Learning workspace.
• C.Embed prompts directly in application configuration files.
• D.Persist prompts in Azure Blob Storage with folder-level organization.

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A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team's organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?

• A.Developer
• B.Data Zone Batch
• C.Data Zone Standard
• D.Global Batch

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Hotspot Question
You manage a Microsoft Foundry project.
You are evaluating two RAG solutions.
When generating answers, the solutions display the following results:
- The first solution displays low completeness and low utilization.
- The second solution displays low completeness and high utilization.
You need to address the issues found during evaluation.
Which action should you perform first for each issue? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

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When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?

• A.Coherence
• B.Textual similarity
• C.Grounded ness
• D.Fluency

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