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| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 2: Operationalizing machine learning solutions | - ML lifecycle management
|
| Topic 3: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 183
You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?
Answer: D
Explanation:
Traffic splitting enables controlled rollout of a new model version by directing a percentage of requests to it. Combined with monitoring alerts, it supports automated rollback when performance degrades. Separate endpoints lack built-in traffic management and do not provide seamless or automated rollback capabilities.
NEW QUESTION # 184
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Add a compute resource to the workspace.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 185
You have an Azure Machine Learning workspace.
You plan to run a job to tram a model as an MLflow model output.
You need to specify the output mode of the MLflow model.
Which three modes can you specify? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C,D
NEW QUESTION # 186
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: A
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 # 187
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 a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
The application retrieves documents from an indexed knowledge base and generates answers for internal users.
Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
Solution: Measure token throughput and average response latency before and after applying the proposed change.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Review user feedback comments collected after deployment to determine whether answers appear more accurate.
Relying solely on user feedback comments is not the proper immediate action for evaluating this specific issue but it is still the best choice.
Evaluate whether your proposed change improves the Retrieval-Augmented Generation (RAG) system by reducing hallucinations (unsupported information), you need to measure faithfulness and context relevance using automated, quantifiable metrics.
The proper course of action is to implement an LLM-as-a-Judge framework using an open-source evaluation library like Ragas or TruLens.
Recommended Evaluation Plan
Establish a baseline: Run your current RAG pipeline through a test dataset of 50-100 representative user queries.
Capture the outputs: Save the user query, the exact retrieved document snippets, and the generated response for every test.
Apply the change: Deploy your proposed modification (e.g., altered prompt, different temperature, or re-ranking algorithm).Run the evaluation: Pass the test dataset through the updated pipeline to generate a new set of responses.
Compare the metrics: Use the framework to score both sets of data and mathematically verify if the change reduced unsupported claims.
Incorrect:
* Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Comparing embedding vector dimensions is not a valid or effective method for measuring RAG answer quality. Vector dimensions (e.g., 1536 or 3072) are static architectural properties of your embedding model. They do not reflect factual accuracy, semantic grounding, or the rate of hallucinations in your text generation.
* Measure token throughput and average response latency before and after applying the proposed change.
Measuring token throughput and latency is not the correct action to solve this specific problem.
Reference:
https://www.getmaxim.ai/articles/how-to-evaluate-your-rag-system/
NEW QUESTION # 188
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