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| Section | Weight | Objectives |
|---|---|---|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| 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 an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
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NEW QUESTION # 100
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 mlstorage 1. The storage account includes a publicly accessible container named mlcontainer 1. 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 mlcontainer 1.
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 # 101
Drag and Drop Question
A customer-facing web application uses a foundational model deployed through Microsoft Foundry.
A new model version must be introduced and validated without disrupting production traffic.
You need to deploy the new version by using a safe promotion strategy.
Which three 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:
NEW QUESTION # 102
You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?
Answer: A
Explanation:
You can use a simulator or an LLM-as-a-judge pipeline to generate synthetic interaction data for fine-tuning. This technique is standard practice for maintaining strict data privacy while training models on specific business tasks.
Here is how to effectively structure and execute a synthetic data generation pipeline for Microsoft Azure AI Foundry (formerly Azure AI Studio).
Generation Methods
Persona-Driven Simulation: Prompt one LLM to act as a customer and another as your support agent to generate multi-turn dialogues.
Seed Data Expansion: Feed 10-20 hand-written, compliant examples into an LLM and instruct it to generate hundreds of diverse variations.
Schema-Based Evolution: Define variables (e.g., product types, user intents, sentiment levels) and programmatically combine them into prompt templates for an LLM to flesh out.
Reference:
https://www.digitaldividedata.com/blog/synthetic-data-generation-in-gen-ai
NEW QUESTION # 103
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
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:
Azure Machine Learning ' s experiment tracking is built around two complementary concepts. First, experiments are named containers that group related runs. By calling mlflow.set_experiment with an experiment name at the start of your training code, all subsequent runs are grouped under that experiment name in the AML workspace, creating the centralized record required. Second, metrics are scalar values such as accuracy, loss, or AUC that represent model performance. Calling mlflow.log_metric with a metric name and value during training persists these values to the run ' s record in the experiment history. These values appear on the Azure ML Studio run detail page and can be compared across runs using the experiment comparison view. Without set_experiment, runs fall into a default experiment. Without log_metric, the run history has no performance data to display or compare.
Microsoft Learn Reference Topic: Track machine learning experiments with MLflow in Azure Machine Learning
NEW QUESTION # 104
Drag and Drop Question
A team performs interactive experimentation during development. The team also runs scalable jobs for model training.
The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.
You need to configure compute targets that support each workload.
Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target 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:
NEW QUESTION # 105
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