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| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding |
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails |
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health |
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NEW QUESTION # 141
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
- accounts for the performance of all previous runs when evaluating the current run
- avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
The Median Stopping Policy and the Truncation Selection Policy are the most appropriate early termination policies for this scenario. Both evaluate runs based on the performance of all previous runs instead of strictly benchmarking against the single best run.
Median Stopping Policy: This policy calculates the running averages of the primary metric across all historical and current training runs at each evaluation interval. It terminates any ongoing run if its performance is worse than the median of those averages. This directly accounts for all previous run performances rather than just the single best run.
Truncation Selection Policy: This policy evaluates all active runs at each interval and terminates a specified bottom percentage (X%) of the lowest-performing runs. Because it aggregates and compares the entire cohort of runs, it avoids making narrow comparisons against only the best- performing run.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 142
You are designing a new machine learning solution to predict customer churn by using Azure Machine Learning. You have raw data in CSV format stored in Azure Data Lake.
You need to design the solution so that it can efficiently handle large-scale model training and iterative development.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point
Answer: B,C
NEW QUESTION # 143
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
Answer: A
Explanation:
The key concept here is low-priority (spot) VMs, which are available at a significant discount because Azure can reclaim them at any time. Azure Machine Learning compute clusters are the only target in the list that directly supports low-priority VM nodes as a cost-saving configuration. You set the minimum and maximum node counts and specify that new nodes should be provisioned as low-priority. Azure Container Instances (ACI) does not support low-priority pricing. Local deployment runs on the developer ' s machine with no Azure billing model. Azure Kubernetes Service (AKS) does support spot node pools but requires significantly more infrastructure management and is not the primary mechanism for low-priority compute in Azure Machine Learning. The exam tests whether you know that AML compute clusters are the managed way to leverage low-priority discounts inside Azure Machine Learning.
Microsoft Learn Reference Topic: Create and manage Azure Machine Learning compute clusters - Low- priority VMs
NEW QUESTION # 144
A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?
Answer: D
Explanation:
Speed of recovery is the central requirement. Rolling back traffic to the previous deployment is the fastest possible action: because the previous deployment still exists on the same endpoint, you simply update the traffic weights - setting the old deployment to 100% and the new deployment to 0% - using a single Azure ML CLI command or SDK call that completes in seconds without reprovisioning any compute. Deleting and redeploying (option B) requires tearing down the endpoint, waiting for deprovisioning, recreating it, re- deploying the model, and waiting for containers to start - potentially 10 to 30 minutes. Changing authentication type (option C) does not affect application errors caused by a bad model. Increasing compute size (option D) does not fix model logic errors. The entire reason Azure ML supports multiple concurrent deployments with traffic splitting is precisely to enable this instant rollback pattern.
Microsoft Learn Reference Topic: Roll back deployments on managed online endpoints - Azure Machine Learning safe deployment practices
NEW QUESTION # 145
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: A
NEW QUESTION # 146
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