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| Section | Objectives |
|---|---|
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering - Evaluate generative AI outputs for quality, safety, and grounding |
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads |
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
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NEW QUESTION # 25
Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
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.
Answer:
Explanation:
NEW QUESTION # 26
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?
Answer: D
Explanation:
Data Zone Standard deployment routes requests to multiple datacenters within a defined geographic zone such as the US data zone, providing lower latency by using zone-level load balancing while maintaining data within a broad compliance boundary. This makes it suitable for enterprise internal applications where multiple business units need low-latency access without strict single-region data residency requirements. Developer deployment (option A) is a low-quota, unguaranteed tier designed for prototype testing, not production workloads serving multiple business units. Data Zone Batch (option B) is designed for high-volume asynchronous batch processing, not interactive low-latency real-time requests. Global Batch (option D) is optimized for scheduled bulk inference across all global regions but adds latency and is inappropriate for synchronous interactive use cases.
Microsoft Learn Reference Topic: Foundation model deployment types in Microsoft Foundry - Data Zone Standard for low-latency internal use
NEW QUESTION # 27
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 # 28
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?
Answer: A
Explanation:
The Content Safety evaluator in Microsoft Foundry is specifically designed to detect hate speech, violence, sexual content, and self-harm content in model outputs, making it the most direct evaluator for the requirement of preventing hateful responses. It uses Azure AI Content Safety service under the hood, which is trained to classify content across these harmful categories with high accuracy. Protected Material (option A) evaluates whether outputs contain copyrighted or licensed material - not hate speech. Groundedness (option B) measures factual accuracy against a source context - completely irrelevant to hate detection. Indirect Attacks (option C) evaluates whether the model was manipulated via prompt injection to produce harmful content - this is a robustness metric, not a direct output quality measure. Content Safety must be configured first because detecting hate is the primary safety concern explicitly identified in the requirement.
Microsoft Learn Reference Topic: Content safety evaluators in Microsoft Foundry - Detect harmful content in AI model outputs
NEW QUESTION # 29
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
Stakeholders must be notified of the drops.
Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
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:
Explanation:
Microsoft ' s guidance on production model monitoring prescribes a four-stage sequence. First, configure an Azure ML model monitor on the deployed endpoint, specifying which metrics to track and on what schedule.
Second, define the threshold value below which model performance is considered unacceptable - this becomes the trigger condition. Third, create an Azure Monitor alert rule that evaluates the monitored metric against the threshold and fires when it is breached; the alert rule is associated with an action group that sends notifications to stakeholders via email, SMS, or Teams webhook. Fourth, the action group includes a webhook action pointing to an Azure Machine Learning pipeline ' s published REST endpoint, which starts the retraining job automatically. This sequence cleanly separates detection, notification, and remediation, matching Microsoft ' s recommended MLOps automation pattern.
Microsoft Learn Reference Topic: Automate model retraining based on monitoring alerts - Azure Machine Learning MLOps
NEW QUESTION # 30
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