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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
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NEW QUESTION # 160
You have an Azure Machine Learning (ML) model deployed to an online endpoint.
You need to review container logs from the endpoint by using Azure Ml Python SDK v2. The logs must include the console log from the inference server with print/log statements from the models scoring script.
What should you do first?
Answer: D
NEW QUESTION # 161
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
Answer: A
Explanation:
In Microsoft Foundry, you can test and compare LLM prompt variants using Prompt Flow. This environment allows you to create a controlled development workflow with consistent inputs, bypassing the need for live user traffic.
1. Generate Synthetic Interaction Data
Generating synthetic data is the recommended first step to build a robust, diverse test dataset without manual effort.
2. Compare Prompt Variants
Once your synthetic dataset is ready, use Prompt Flow to iterate on your prompts.
3. Controlled Evaluation
To measure performance objectively, run automated Eval Runs.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/concepts/prompt-flow
NEW QUESTION # 162
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities. You may use each tool 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:
Implement variants: LLM tool
Invoke a custom API: Python tool
For implementing variants , use the LLM tool . Microsoft documents that Prompt flow variants are supported specifically on LLM tool nodes. A variant represents an alternative configuration of the same node, such as different prompt text, temperature values, deployment settings, or other LLM parameters. This allows multiple prompt/model configurations to be tested without duplicating the entire flow, which directly minimizes development effort.
For invoking a custom API , use the Python tool . The Python tool executes custom Python logic within a Prompt flow node and can integrate with external or third-party services. Microsoft explicitly describes Prompt flow tools as supporting integration with third-party APIs and Python packages, and the Python tool can also consume a custom connection when authentication credentials are required. This makes it the appropriate choice for calling an API that is not covered by a built-in Prompt flow tool.
The Embedding tool is designed to generate vector embeddings for text and is therefore unrelated to either requirement. It does not provide variant functionality and is not the general-purpose mechanism for invoking a custom API.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Prompt flow tools, LLM variants, Python nodes, custom integrations, and flow experimentation.
NEW QUESTION # 163
A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
To host generative AI workloads in a Microsoft Foundry environment with strictly private communication and no public network exposure, you must configure a Managed Virtual Network (Managed VNet) with specific isolation settings and disable all public inbound access.
[A]
Enable Managed Virtual Network Isolation
During the creation of your Azure AI Foundry hub, navigate to the Networking tab.
Select the Private with Approved Outbound isolation mode. This mode ensures that all outbound traffic from the managed compute resources is restricted to only the destinations you explicitly approve, such as dependent Azure resources.
Once enabled, this isolation mode cannot be disabled.
[E]
Disable Public Inbound Access
In the Networking tab of your Foundry resource, set Public network access to Disabled.
This action blocks all traffic from the public internet, ensuring the resource is only accessible through private connections.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/managed-virtual-network
NEW QUESTION # 164
-
You have a Microsoft Foundry project with a connected Azure OpenAI Service model.
You have a set of text files stored locally on your computer.
You must set up a flow that will generate responses based on the content of your local files.
You need to implement a solution.
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:
Correct sequence:
* Create a data asset.
* Create a Foundry Search resource.
* Create a vector index.
* Create a flow.
First, create a data asset so that the locally stored text files are available within the project as a managed data source. The content must then be made searchable for Retrieval-Augmented Generation.
Next, create a Foundry Search resource to provide the search infrastructure that will store and serve the indexed document content. Microsoft documents Azure AI Search as a supported vector store for Foundry and Azure Machine Learning RAG workloads.
Third, create a vector index from the source documents. During vector-index creation, the content is processed into chunks and embeddings, which enables semantic similarity retrieval. Microsoft documents that vector indexes can be created from local files, folders, or registered data assets and then consumed by an Index Lookup operation.
Finally, create a flow and configure it to query the vector index and supply the retrieved context to the connected Azure OpenAI model. Microsoft specifically documents adding an existing vector index to a prompt flow through the Index Lookup tool.
Create a connection is the unused action in this question because the scenario already specifies a connected Azure OpenAI Service model.
Study Guide Reference: Design and implement a GenAIOps infrastructure - RAG architecture, project data, vector indexing, Azure AI Search, prompt flow, and grounding with enterprise content.
NEW QUESTION # 165
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