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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 4: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Topic 5: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
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NEW QUESTION # 124
You create an Azure Machine Learning workspace named workspaces. You create a Python SDK v2 notebook to perform custom model training in workspace1. You need to run the notebook from Azure Machine Learning Studio in workspace1. What should you provision first?
Answer: B
NEW QUESTION # 125
You need to configure an optimization method to meet Fabrikam Inc.'s technical requirements.
Which strategy should you apply first? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Domain specialization: Supervised fine-tuning
Poor response accuracy of a RAG-based solution: Apply prompt engineering For domain specialization , Supervised Fine-Tuning (SFT) is the correct first strategy. Microsoft describes SFT as the foundational fine-tuning technique for training a model from labeled input-output pairs , and specifically identifies domain specialization as one of its principal use cases. Microsoft also recommends starting with SFT for most customization projects because it supports task specialization, instruction following, style, and domain-specific behavior. Fabrikam already possesses evaluation data containing input- output pairs, which aligns directly with the SFT data model.
For poor RAG response accuracy , the first action is prompt engineering . The case explicitly requires advanced fine-tuning only when prompt engineering is insufficient. In a RAG system, prompt engineering determines how the model interprets retrieved context, constrains answers to grounding information, handles missing evidence, and formats responses. Microsoft notes that inadequate RAG prompting can produce false or incomplete answers even when retrieval returns appropriate content.
DPO is primarily appropriate for alignment using preferred versus non-preferred responses, while RFT targets complex reward-based reasoning optimization. Neither is the initial technique for domain specialization in this requirement.
Study Guide Reference: Optimize generative AI systems and model performance - prompt engineering, RAG optimization, supervised fine-tuning, preference optimization, and model customization strategy.
NEW QUESTION # 126
You create an Azure Machine Learning workspace and a new Azure DevOps organization. You register a model in the workspace and deploy the model to the target environment.
All new versions of the model registered in the workspace must automatically be deployed to the target environment.
You need to configure Azure Pipelines to deploy the model.
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:
Step 1: Create an Azure DevOps project
Step 2: Create a release pipeline
Sign in to your Azure DevOps organization and navigate to your project.
Go to Pipelines, and then select New pipeline.
Step 3: Install the Machine Learning extension for Azure Pipelines
You must install and configure the Azure CLI and ML extension.
Step 4: Create a service connection
How to set up your service connection
Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.
Note: How to enable model triggering in a release pipeline
Go to your release pipeline and add a new artifact. Click on AzureML Model artifact then select the appropriate AzureML service connection and select from the available models in your workspace.
Enable the deployment trigger on your model artifact as shown here. Every time a new version of that model is registered, a release pipeline will be triggered.
Reference:
https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml
https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning
NEW QUESTION # 127
A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?
Answer: B
NEW QUESTION # 128
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
Answer: C
NEW QUESTION # 129
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