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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
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NEW QUESTION # 29
A team is validating a generative AI assistant for a company. The assistant generates responses by using internal knowledge sources.
The company requires assurance that responses are accurate, supported by sources, and related to the user prompts before enabling production access.
You need to implement quality metrics that confirm the assistant produces reliable and meaningful responses.
Which two evaluation metrics should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
To ensure your Azure Machine Learning generative AI assistant (which utilizes a Retrieval- Augmented Generation or RAG architecture) produces reliable and meaningful responses before going live, you should use the RAG Triad of built-in quality evaluation metrics.
These primary automated metrics-Groundedness, Relevance, and Response Completeness- directly measure accuracy, connection to internal knowledge sources, and alignment with user prompts.
The Primary Metrics (The RAG Triad)
These metrics are evaluated on a 1-to-5 scale using Azure Machine Learning's built-in, AI- assisted "LLM-as-a-judge" evaluators:
Groundedness: Measures how well the assistant's generated answer aligns only with the information retrieved from your internal knowledge sources. Even if a response is factually correct in the real world, it is penalized if the information cannot be verified inside the retrieved context document. This is your primary defense against hallucinations.
Relevance: Assesses how pertinently the model's generated response directly addresses the user's specific prompt. This checks whether the system understood the user's intent or if it provided an off-topic or distracted response.
Response Completeness: Focuses on the "recall" aspect of the assistant. It measures whether the generated text effectively answers all parts of the user prompt using the ground truth data, ensuring no critical insights or data points are omitted.
Reference:
https://medium.com/thedeephub/a-deep-dive-into-evaluation-in-azure-prompt-flow-dd898ebb158c
NEW QUESTION # 30
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
Answer: C
NEW QUESTION # 31
Drag and Drop Question
A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 32
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: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Incorrect:
* Create prompt variants and compare their outputs in the Evaluation experience.
* Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Note:
In Azure AI Foundry, you can capture and compare these metrics by enabling Tracing and using the Bulk Test feature. This allows you to systematically evaluate different prompt variants against a common dataset.
Steps to Evaluate and Compare Prompt Variants
*-> 1. Enable Tracing
Navigate to your Prompt Flow project.
Locate the Tracing toggle at the top of the flow authoring page.
Switch it to On.
This ensures every execution captures latency, token counts, and node-level inputs/outputs.
2. Create Prompt Variants
Within your flow, identify the LLM node you want to test.
Click Variants to create multiple versions of your prompt (e.g., Variant_0, Variant_1).
This allows you to test different instructions or few-shot examples side-by-side.
3. Run a Bulk Test (Evaluation)
4. Analyze the Results
Reference:
https://www.linkedin.com/pulse/streamlining-generative-ai-development-azure-foundry-tracing- taneja-mbwze
NEW QUESTION # 33
A team manages prompts that are used by a generative AI application built on Microsoft Foundry. Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
The team requires that:
Prompt changes are reviewed before being applied to the version in production.
Previous prompt versions can be restored if issues occur.
Prompt updates follow the same governance practices as the application code.
You need to implement a controlled process for managing and updating prompts in production.
How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content . NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
All three requirements point to Git as the underlying mechanism, but each uses a different Git capability. For reviewing changes before production: a branch-based workflow where prompt changes are made on feature branches and merged to the main branch only after pull request approval enforces the review gate. For restoring previous versions: Git ' s commit history and tag system provide a precise, immutable record of every prompt state, and a git revert or checkout to a specific commit SHA restores any prior version instantly.
For governance parity with application code: by storing prompts in the same Git repository as application code, all the same CI/CD, branch protection, code review, and audit trail policies apply automatically. The alternatives such as Blob Storage or embedded configuration files lack native review workflows, branch protection, and full audit history.
Microsoft Learn Reference Topic: Prompt management and versioning with Git integration in Microsoft Foundry
NEW QUESTION # 34
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