You need to standardize how Fabrikam Inc. manages machine learning assets.

Which action should you perform first?

You need to recommend an experiment-tracking strategy that ensures consistent experiment results.

What should you recommend?

You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.’s issues, constraints, and technical requirements.

What should you implement?

You manage a Retrieval-Augmented Generation (RAG) system that uses Azure AI Search to retrieve documents from an indexed knowledge base.

The system must support the following retrieval requirements:

Queries that include exact policy identifiers must return matching documents even when semantic similarity is low.

Natural-language questions must prioritize semantically relevant documents even when keywords are not an exact match.

You need to configure the retrieval approach to meet the requirements.

How should you configure the retrieval behavior for each requirement? To answer, select the appropriate options in the answer area . NOTE: Each correct selection is worth one point.

A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.

The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.

You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.

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.

An organization validates generative AI applications during CI/CD Microsoft Foundry.

Evaluation must run automatically and block releases when quality thresholds are NOT met. Manual evaluation is no longer acceptable.

Evaluation must use both predefined quality metrics and custom safety checks.

You need to implement an automated evaluation workflow that supports both built-in and custom metrics .

What should you do?

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?

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 manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data.

The training_data argument specifies the path to the training data in a file named dataset 1. csv.

You plan to run the script.py Python script as a command job that trains a machine learning model.

You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.

Solution: python script.py --trainingdata ${{inputs.training_data}}

Does the solution meet the goal?

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?