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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 2: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 3: Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Topic 4: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 5: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
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NEW QUESTION # 39
You deploy a model to production but do not have labeled data available for evaluating prediction accuracy. However, you must monitor model health continuously. What is the BEST strategy?
Answer: B
Explanation:
When labeled data is unavailable, traditional accuracy metrics cannot be computed. Data drift detection monitors changes in input data distribution, serving as a proxy for potential performance degradation. This allows early detection of issues before labeled data becomes available.
NEW QUESTION # 40
Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 41
Hotspot Question
You manage a Microsoft Foundry project.
You are developing a solution to generate content based on text and images. The solution requires the ability to manage high-volume processing and avoid disruptions to the online workloads.
You need to deploy the solution.
Which deployment type and large language model (LLM) should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Using Global provisioned deployment and a GPT-4o model is an excellent, industry-standard choice for this Microsoft AI Foundry project. This approach ensures you meet your high-volume processing and text-and-image generation requirements without degrading online workloads, offering both guaranteed latency and dedicated resources.
Box 1: GPT-4o
The GPT-4o Model is highly optimized for handling both text and image input. It provides the multi-modal reasoning necessary to generate contextually accurate content based on visual and textual datasets.
Box 2: Global provisioned
Provisioned Deployment Type: Provisioned throughput allows you to allocate dedicated compute capacity (tokens per minute). This guarantees performance and is explicitly designed to handle high-volume processing without disrupting your online workloads, as you will not be sharing constrained resources.
Global vs. Regional: Global provisioned deployments decouple capacity management from specific regions, providing the highest throughput limits and automatic access to resources with predictable costs. However, if your enterprise has strict European data residency regulations, choosing a Regional Provisioned deployment in a local region ensures your traffic and data are processed entirely within the EU.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/provisioned-throughput
NEW QUESTION # 42
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 # 43
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For deployment failures when containers do not start, review deployment logs in Azure ML Studio or use the CLI get-logs command to surface dependency installation errors, missing files, or scoring script import failures. For authentication errors such as 401 or 403 responses, check that client applications are using valid authentication keys or bearer tokens, as mismatched or expired credentials cause auth failures. For performance degradation and slow responses, review compute instance scaling configuration, since a surge in traffic saturates a fixed number of replicas causing queuing and high latency without autoscaling. For internal server errors such as HTTP 500, these typically originate in the scoring script, so inspect the init and run functions for unhandled exceptions and verify the environment has all required packages installed.
Microsoft Learn Reference Topic: Troubleshoot online endpoint deployment failures - Azure Machine Learning
NEW QUESTION # 44
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