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| Section | Weight | Objectives |
|---|---|---|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
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NEW QUESTION # 155
A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?
Answer: D
Explanation:
Chunk size and overlap directly affect the precision and completeness of information retrieved in a RAG architecture. Microsoft identifies effective chunking as a critical part of successful RAG implementation. If chunks are too large, they can contain excessive unrelated information, reducing retrieval precision because the vector representation covers multiple concepts. Conversely, chunks that are too small can omit necessary surrounding context or separate related information across chunk boundaries, resulting in incomplete retrieval.
Overlap helps preserve semantic continuity between adjacent chunks. Microsoft recommends using overlap particularly with fixed-size chunking because relevant facts or concepts can span chunk boundaries. The optimal chunk size and overlap should therefore be evaluated against representative documents and queries rather than treated as fixed constants. Microsoft specifically notes that larger chunks can preserve contextual richness, while appropriately configured overlap prevents contextual information from being lost at segmentation boundaries.
Temperature controls generation variability , not document retrieval. Token limits constrain how much content a model processes but do not inherently make retrieval more relevant. Embedding strategy can also influence retrieval quality, but given the stated symptom of irrelevant or incomplete retrieved passages , tuning chunk size and overlap is the directly targeted remediation.
Study Guide Reference: Optimize generative AI systems and model performance - RAG optimization, document chunking, retrieval precision, contextual completeness, and grounding.
NEW QUESTION # 156
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage1. The storage account includes a publicly accessible container named mlcontainer1. The container stores 10 blobs with files in the CSV format.
You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer1.
You need to complete the Python SDK v2 code.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Because you want to reference all blobs in a container rather than a single file, the correct asset type is AssetTypes.URI_FOLDER. This type points to a directory-level URI, allowing Azure ML to traverse all files within it. For a publicly accessible Azure Blob Storage container, the URI follows the pattern
https://storage_account.blob.core.windows.net/container_name/. You then create the Data object with the correct type, path, name, and version parameters, and register it in the workspace using ml_client.data.
create_or_update. Do not use AssetTypes.URI_FILE, which references a single file. Do not use AssetTypes.
MLTABLE unless you have an MLTable YAML descriptor. URI_FOLDER is the correct choice for referencing a collection of CSV blobs in an Azure Blob Storage container, allowing Azure ML to discover and process all files within the specified path.
Microsoft Learn Reference Topic: Create and manage data assets in Azure Machine Learning Python SDK v2
- URI_FOLDER
NEW QUESTION # 157
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Clone an existing Azure Machine Learning workspace to create additional environments.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
This action is best because it fulfills all of your operational requirements:
Consistency & Controlled Manner:
Infrastructure as Code (IaC) via Bicep ensures that dev, test, and prod environments are configured identically, eliminating configuration drift.
Source Control: Storing the Bicep template in a Git repository satisfies the requirement that each environment must be updated through source control.
Automated & Repeatable: Orchestrating the deployment using GitHub Actions fully automates the workflow, allowing it to be executed reliably every time a change is merged.
Auditable: Git commit histories combined with GitHub deployment logs provide a comprehensive, compliant audit trail of exactly who modified the infrastructure and when.
Incorrect:
* Clone an existing Azure Machine Learning workspace to create additional environments.
* Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
NEW QUESTION # 158
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?
Answer: C
Explanation:
The first action is to register the model in the Azure Machine Learning workspace . Microsoft's documented no-code workflow for generating Responsible AI insights in Azure Machine Learning studio explicitly starts by registering the model. After registration, you navigate to Models , select the registered model, and choose Create Responsible AI dashboard .
The scenario already specifies an MLflow-formatted regression model using tabular data , which aligns with the Responsible AI dashboard's supported model architecture. Microsoft states that models used by the Responsible AI components must be registered in Azure Machine Learning in MLflow format with an sklearn flavor , and the associated datasets must use supported tabular formats such as MLTable.
Option B is incorrect because model explanations are an optional dashboard component configured after selecting the registered model; they are not the prerequisite first step. Option C is unnecessary and counterproductive because MLflow is a supported format. Option D is also unnecessary: a Responsible AI dashboard evaluates a registered model and does not require deployment to a managed online endpoint first.
Microsoft also describes Responsible AI dashboards as being linked directly to registered models in the model registry.
Study Guide Reference: Implement machine learning model lifecycle and operations - model registration, MLflow model management, Responsible AI dashboards, model evaluation, and Azure Machine Learning studio.
NEW QUESTION # 159
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named model_train.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: command_component
The @command_component decorator transforms a standard Python function into a reusable pipeline component within Azure ML SDK v2.from model_ Box 2: model_train model_train import train: Because model_train.py resides in the same directory as train_component.py, you import the file directly by its module name (model_train) to access its execution logic inside the component function.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-component-pipeline-python
NEW QUESTION # 160
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