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| Section | Objectives |
|---|---|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
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NEW QUESTION # 22
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?
Answer: B
Explanation:
Navigate to the Microsoft Foundry project in the Azure portal or Foundry portal and open the prompt flow for which you want to capture telemetry. Locate the Tracing toggle in the project or flow settings and enable it - this single action instructs the Foundry runtime to instrument every LLM call within the flow. Execute test runs of the prompt flow through the portal ' s test interface or via the CLI. Open the Traces view in Microsoft Foundry: each run appears as a trace with a tree of spans, where each span corresponds to one LLM call or tool invocation. Every span captures the exact input sent to the model, the model ' s output, token counts including prompt tokens and completion tokens, and wall-clock latency. Compare runs across prompt variants by examining individual traces side-by-side. This solution directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Enable and use tracing in Microsoft Foundry - Capturing LLM call telemetry for evaluation
NEW QUESTION # 23
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:
You need to complete the Python code to log the table.
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:
Explanation:
Explanation:
NEW QUESTION # 24
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.
Answer: C,E
Explanation:
Groundedness and Relevance directly measure the two quality characteristics specified in the requirement.
Groundedness evaluates whether the generated response is supported by the supplied knowledge context rather than introducing unsupported or fabricated information. Microsoft Foundry defines the Groundedness evaluator as measuring how well a generated response aligns with the provided context without fabricating content. For an assistant that relies on internal knowledge sources, this is the primary metric for determining whether its claims are traceable to retrieved evidence.
Relevance evaluates whether the generated response actually addresses the user ' s query. Microsoft defines the Relevance evaluator as measuring how relevant the response is to the query, including whether it appropriately and accurately responds to the user ' s request. This detects responses that may be factually grounded yet tangential or insufficiently focused.
Harmfulness and Fairness are safety-oriented dimensions rather than measures of factual support or query alignment. Tone assesses communication characteristics but does not establish whether a response is supported by source material or relevant to the prompt.
Therefore, the required pair is Groundedness + Relevance .
Study Guide Reference: Implement generative AI quality assurance and observability - Foundry quality evaluators, groundedness, relevance, RAG evaluation, and pre-production validation.
NEW QUESTION # 25
A team runs training and inference jobs in Azure Machine Learning.
The team experiences inconsistent runtime dependencies that cause variation in results.
You need to ensure that all jobs use the same execution dependencies.
Which asset should you define?
Answer: B
Explanation:
To guarantee consistent runtime dependencies in Azure Machine Learning, you must use Azure ML Environments configured with custom Docker images or pinned Conda dependencies.
An Environment asset encapsulates the exact Python packages, environment variables, and software settings for your training and inference workloads.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets
NEW QUESTION # 26
You manage an Azure Machine Learning workspace that includes a batch endpoint. You plan to deploy a model to the batch endpoint. You need to configure compute for the deployment. Which compute should you use?
Answer: C
NEW QUESTION # 27
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