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| Section | Objectives |
|---|---|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Implement secure and scalable AI systems | - Security and governance
|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
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NEW QUESTION # 175
Hotspot Question
A team deploys a generative AI application built by using Microsoft Foundry to production and receives variable traffic throughout the day.
The team requires uninterrupted insight into the application s health and model behavior to detect issues without relying on manual inspection.
You need to select the monitoring capabilities that provide real-time operational visibility into the application.
Which monitoring capability should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Real-time tracing
Observe live request flow.
To best observe the live request flow in your generative AI application, you should use Real-time tracing.
Real-time tracing maps the end-to-end journey of a request as it flows through various microservices, APIs, and AI model endpoints. It provides the exact visibility needed to pinpoint bottlenecks, latencies, and failures in the live request path immediately.
Box 2: Aggregated metrics
Identify recurring failure patterns.
To best identify recurring failure patterns in a generative AI application with variable traffic, you should use aggregated metrics.
Aggregated metrics groups data points over specified time intervals to reveal long-term trends, error rates, and system anomalies. It allows you to track patterns (like a spike in 5xx errors or model timeouts during peak traffic) over hours or days without manually inspecting individual logs.
Reference:
https://docs.azure.cn/en-us/ai-services/metrics-advisor/glossary
NEW QUESTION # 176
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:
The Evaluation experience in Microsoft Foundry ' s prompt flow editor shows aggregate quality scores across prompt variants - which variant produces more coherent answers, which scores higher on groundedness.
However, it does not expose per-run raw telemetry: individual token counts per call, per-request latency in milliseconds, or the exact input-output pairs for each execution. The Evaluation experience is designed for comparative quality scoring, not for detailed operational telemetry. To capture inputs, outputs, token usage, and latencies at the granular run level, Tracing must be enabled in Microsoft Foundry. Tracing records each LLM call as a structured span with timing, token consumption, and the complete input-output payload - a fundamentally different view than evaluation scores that directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Trace and debug prompt flows in Microsoft Foundry - Tracing vs.
Evaluation
NEW QUESTION # 177
Drag and Drop Question
You have a Microsoft Foundry project with a connected Azure OpenAI Service model.
You have a set of text files stored locally on your computer.
You must set up a flow that will generate responses based on the content of your local files.
You need to implement a solution.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 178
You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.
You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.
You need to continue to log metrics and artifacts from your data science code.
What should you do?
Answer: C
NEW QUESTION # 179
Hotspot Question
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.
You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 180
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