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| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications |
| Topic 2: Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
| Topic 3: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 4: Design and implement an MLOps infrastructure | - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 5: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases |
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NEW QUESTION # 134
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio. You must preserve the current values of variables set in the notebook for the current instance.
You need to maintain the state of the notebook.
What should you do?
Answer: D
Explanation:
To preserve the current values of variables set in the notebook and maintain the state for the current instance, you should stop the current kernel.
Memory State Retention: In Azure Machine Learning Studio notebooks, stopping the notebook kernel halts execution while allowing the compute instance to continue running.
Avoiding State Loss: While stopping or resetting the entire compute instance will completely clear your active in-memory variables and notebook state, cleanly stopping the kernel natively preserves your current progress and data states within that active notebook session.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/quickstart-create-resources
NEW QUESTION # 135
Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?
Answer: A
Explanation:
A model registry provides centralized management of models, including versioning, lineage tracking, and governance. This enables teams to discover, share, and reuse models efficiently.
Simple storage solutions like blob storage lack these advanced capabilities and do not support proper lifecycle management.
NEW QUESTION # 136
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 # 137
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow. You are reviewing the results of experiment1 by using the following code segment:
For each of the following statements, Select Yes if the statement is true Otherwise, select No.
Answer:
Explanation:
Explanation:
NEW QUESTION # 138
You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,C,E
Explanation:
To reset the state of a Python SDK v2 notebook in Azure Machine Learning Studio, you can perform any of the following three individual actions:
Stop the current kernel
Reset the compute instance
Change the current kernel
Each of these steps acts as a standalone solution to clear the current runtime memory, variables, and overall execution state of your notebook.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks
NEW QUESTION # 139
......
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