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| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering |
| Topic 2: Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 3: Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput |
| Topic 4: Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production |
| Topic 5: Design and implement an MLOps infrastructure | - 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 - Manage environments, data stores, and model registries |
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NEW QUESTION # 163
You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace. You need to submit a pipeline run from the designer.
What should you do first?
Answer: C
NEW QUESTION # 164
A team deploys a generative AI application that uses a model deployed in Microsoft Foundry.
The application must support latency monitoring under production load.
You need to enable performance observability.
Which three 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 observability guidance for Azure AI Foundry applications describes a three-stage activation sequence. First, enable Tracing in the Microsoft Foundry project settings before deployment - Tracing instruments the application ' s LLM calls with OpenTelemetry-compatible spans that capture timing data for each step in the flow. Second, deploy the application to a production endpoint so that real traffic flows through the instrumented code path - without actual production traffic, there is no latency data to observe.
Third, configure Azure Monitor and Application Insights to receive, aggregate, and visualize the telemetry emitted by Tracing. Azure Monitor ' s metrics explorer and Application Insights ' performance views display p50, p95, and p99 latency distributions over time, enabling the team to identify latency regressions and set alert thresholds. This sequence - instrument, deploy, visualize - is the canonical Microsoft path to production AI performance observability.
Microsoft Learn Reference Topic: Monitor generative AI applications with Azure Monitor and Microsoft Foundry Tracing
NEW QUESTION # 165
You manage an Azure Machine learning workspace named workspace1.
You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class.
You need to add the instantiated compute instance to workspace 1.
What should you use?
Answer: A
NEW QUESTION # 166
Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:
You need to determine whether the fine-tuned model shows improved performance without introducing regression. 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 # 167
A team provisions an Azure Machine Learning environment by triggering pull requests.
Deployments must be automated, auditable, and require approval before running.
You need to select a deployment automation tool.
Which tool should you use?
Answer: A
Explanation:
GitHub Actions is the appropriate automation tool because the deployment process originates from pull requests and requires CI/CD automation, traceability, and an approval gate. Azure Machine Learning integrates directly with GitHub Actions for automated machine learning workflows, including infrastructure and asset deployment. Workflows can be triggered by repository events such as pull requests, merges, or pushes, providing an auditable record of who initiated the change, which commit was deployed, and whether the workflow succeeded.
Critically, GitHub environments can enforce required reviewers before a deployment job executes. Microsoft documents that when an environment requires approval, a job cannot access the environment ' s protected secrets until one of the required reviewers approves the deployment. This directly satisfies the requirement that deployment must require approval before running.
Azure Machine Learning pipelines orchestrate ML workflow steps such as preprocessing, training, evaluation, and registration, but they are not primarily the repository-level pull-request approval mechanism. MLflow provides experiment tracking and model lifecycle functionality, not CI orchestration. Azure Monitor provides telemetry and alerting rather than source-driven deployment automation.
Therefore, GitHub Actions provides the required combination of pull-request integration, automation, auditability, and protected deployment approval.
Study Guide Reference: Design and implement an MLOps infrastructure - CI/CD, GitHub Actions, pull- request automation, deployment approvals, protected environments, and Azure Machine Learning integration.
NEW QUESTION # 168
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