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| Section | Weight | Objectives |
|---|---|---|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
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NEW QUESTION # 154
Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy for an Azure ML Python SDK v2-based model training.
The policy uses a slack_factor set to 0.1, an evaluation interval set to 1, and an evaluation delay set to 5.
You need to evaluate the outcome of the early termination policy.
What should you evaluate? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: 91%
Box 2: Every interval when metrics are reported, starting at evaluation interval 5 Every interval when metrics are reported, starting at evaluation interval 5 is the best description for the run termination schedule in this scenario.
evaluation_interval = 1: The policy checks for potential termination every time the training script logs the primary metric.
evaluation_delay = 5: Policy evaluation is suspended for the first 5 intervals to prevent the premature termination of training runs before they have time to stabilize.
Combined Behavior: Once the evaluation_delay threshold of 5 is met, the policy applies at every subsequent multiple of the evaluation_interval (Intervals 5, 6, 7, etc.).
Reference:
https://azure.github.io/azureml-sdk-for-r/reference/bandit_policy.html
NEW QUESTION # 155
You use the Azure Machine Learning SDK v2 for Python and notebooks to train a model. You use Python code to create a compute target an environment and a training script You need to prepare information to submit a training job. Which class should you use?
Answer: A
NEW QUESTION # 156
-
A team is standardizing MLOps practices by using automated deployments.
The team requires infrastructure to be defined declaratively and deployed through automation pipelines.
You need to configure infrastructure deployment.
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:
Explanation:
Deploy resources from a pipeline: Azure CLI Commands
Define Azure resources declaratively: Bicep templates
Azure CLI Commands should be used to deploy resources from an automation pipeline. Azure Machine Learning supports integration with CI/CD platforms such as Azure DevOps and GitHub Actions, and Microsoft documents the Azure CLI with the Machine Learning extension as a standard mechanism for automating resource provisioning, training pipelines, model deployment, and other MLOps operations. Azure CLI commands can be executed non-interactively within pipeline stages, making them appropriate for repeatable automated deployments.
Bicep templates should be used to define Azure infrastructure declaratively. Bicep is Microsoft ' s domain- specific Infrastructure-as-Code language for Azure Resource Manager. Instead of specifying individual imperative provisioning steps, a Bicep file describes the desired state of Azure resources. Azure Resource Manager then determines the deployment operations required to reach that state. This provides repeatable, version-controlled, consistent infrastructure across development, testing, and production environments.
Bicep deployments can also be invoked directly through Azure CLI commands such as az deployment group create, allowing the declarative infrastructure definition and automated deployment mechanism to work together in an MLOps CI/CD pipeline.
Study Guide Reference: Design and implement an MLOps infrastructure - Infrastructure as Code, Bicep, Azure CLI, automated deployment pipelines, and reproducible environment provisioning.
NEW QUESTION # 157
Hotspot Question
A team is provisioning a new Azure Machine Learning workspace for a production project.
The workspace must support secure secret storage and operational monitoring. The team requires the workspace to be created with the correct dependent resources to meet security and monitoring requirements.
You need to configure the required dependencies when the team creates the workspace.
Which resources should you associate with the workspace? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 158
Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
- Retrieved results frequently include duplicated content from the same document.
- Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections. 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 # 159
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