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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 3: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Topic 4: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 5: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
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NEW QUESTION # 67
An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads.
The organization has the following workload requirements:
* One workload must be real-time, latency-sensitive, and have predictable global usage patterns that demand consistent performance.
* One workload must have variable performance and be optimized for cost-efficient operation.
You need to select a global deployment type for each workload.
Which type of deployment should you use for each workload requirement? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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:
Explanation:
Real-time, predictable: Global Provisioned
Flexible, cost efficient: Global Standard
For the real-time, latency-sensitive workload with predictable global demand , use Global Provisioned .
Microsoft Foundry defines Global Provisioned deployments as providing reserved model processing capacity and predictable throughput through provisioned throughput units (PTUs). Because capacity is reserved, this deployment type provides lower and more consistent latency than Global Standard and is specifically recommended for sustained, predictable, high-throughput production workloads.
For the workload requiring flexibility and cost-efficient operation , use Global Standard . Global Standard operates on a pay-per-token model and dynamically routes requests across Azure ' s global infrastructure.
Microsoft recommends it as the starting deployment type for most workloads because it offers the lowest price, broad regional availability, and high default quota without requiring the customer to reserve PTU capacity. This makes it appropriate when demand varies and paying for permanently reserved throughput would be inefficient.
The architectural distinction is therefore straightforward: Global Provisioned trades reserved-capacity cost for predictable throughput and reduced latency variance, while Global Standard provides elastic, consumption-based operation with greater latency variability under sustained high load. Microsoft's deployment overview likewise identifies provisioned throughput for predictable low-latency performance and standard deployment for general hosted workloads.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Microsoft Foundry deployment types, Global Standard, Global Provisioned, PTUs, latency predictability, throughput, and cost optimization.
NEW QUESTION # 68
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
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:
For tracking changes across contributors, Git integration is the answer: by connecting the Microsoft Foundry project to a Git repository, every prompt file change is tracked as a commit with author attribution, timestamp, and diff view, and pull requests enforce review before changes reach production. The Git history provides the complete audit trail and rollback capability needed for traceability. For allowing applications to consume updated prompts without requiring redeployment, Microsoft Foundry ' s prompt management feature allows prompts to be stored and versioned as named artifacts in the project. Applications reference prompts by name and load the latest approved version at inference time, rather than having prompt text hard-coded in the application deployment artifact. This decoupling means updating a prompt is a content operation - not a code deployment - so applications automatically pick up the new prompt without any redeployment.
Microsoft Learn Reference Topic: Prompt management in Microsoft Azure AI Foundry - Git integration and dynamic prompt versioning
NEW QUESTION # 69
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?
Answer: A
Explanation:
For preparing a multi-turn training data file for the Azure OpenAI Service, you should use UTF-8 with a Byte Order Mark (BOM) encoding.
File Format Requirements
Format: The file must be in JSON Lines (JSONL) format, where each individual line is a valid JSON object representing one training example.
Encoding: Specifically, Azure OpenAI requires the JSONL file to be encoded in UTF-8 with BOM.
Structure: For multi-turn conversations, each line must contain a messages array with multiple role ("system", "user", "assistant") and content pairs to represent the dialogue history.
Reference:
https://dev.to/icebeam7/fine-tuning-a-model-with-azure-open-ai-studio-39p7
NEW QUESTION # 70
You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?
Answer: D
Explanation:
The correct parameter is image . In Azure Machine Learning Python SDK v2, the Environment entity supports defining an execution environment directly from an existing Docker image by specifying the image URI through the image parameter . Microsoft provides the explicit SDK v2 pattern:
Environment(image= " pytorch/pytorch:latest " , ...)
The resulting environment can then be registered by using ml_client.environments.create_or_update().
The image value can reference an image hosted in a supported container registry such as Docker Hub or Azure Container Registry. Azure Machine Learning then uses that image as the container environment for training or deployment workloads.
build is different: it is used when Azure Machine Learning must build a Docker image from a Docker build context , typically a directory containing a Dockerfile and related files. Microsoft specifies that image and build represent alternative environment-definition approaches.
conda_file specifies Python and Conda dependencies layered on top of a base Docker image; it does not identify the Docker image itself. properties stores arbitrary metadata and cannot define the execution container.
Therefore, to create an SDK v2 environment directly from an existing Docker image, use image .
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning environments, Python SDK v2, Docker images, reusable execution environments, and dependency management.
NEW QUESTION # 71
You manage an Azure Machine Learning workspace. You use Azure Machine Learning Python SDK v2 to configure a trigger to schedule a pipeline job. You need to create a time-based schedule with recurrence pattern.
Which two properties must you use to successfully configure the trigger? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: A,C
NEW QUESTION # 72
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