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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - Large language model integration
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
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NEW QUESTION # 122
You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?
Answer: D
Explanation:
To target and install Python packages into the currently running kernel only, you should use the %pip line magic function.
Kernel Isolation: Unlike using the shell command !pip install, which installs the package into the underlying compute instance's base environment, the %pip magic explicitly targets the specific Python executable assigned to your active notebook kernel.
No Restart Needed: It automatically syncs the newly installed packages with the current session so you can import them immediately without restarting the kernel.
Example Usage
Simply type and run the following python line in a notebook cell:
%pip install <package_name>
Reference:
https://learn.microsoft.com/en-us/answers/questions/1921271/i-have-a-new-compute-instance-that-has-python3-8-a
NEW QUESTION # 123
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.
Which file format should you use?
Answer: D
Explanation:
Azure OpenAI Service fine-tuning requires training data in JSON Lines (JSONL) format - a text file where each line is a self-contained, valid JSON object representing one training example. For chat fine-tuning, each line contains a messages array with system, user, and assistant turns. JSONL is ideal for machine-learning datasets because it is streamable, easy to generate programmatically, and directly supported by the Azure OpenAI fine-tuning API. CSV (option A) and TSV (option B) are tabular formats that cannot natively represent the nested, multi-turn conversation structure required by chat models. Plain JSON (option D) would require loading the entire file as a single object, which is impractical for large datasets and is not accepted by the Azure OpenAI fine-tuning endpoint. Always use JSONL with UTF-8 encoding for Azure OpenAI training files.
Microsoft Learn Reference Topic: Prepare training data for Azure OpenAI fine-tuning - JSONL file format
NEW QUESTION # 124
A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
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,D
Explanation:
[A]
Engineer Permissions (Hub Scope)
Engineers require the ability to manage infrastructure, networking, and global security settings.
Role: Azure AI Administrator or Contributor.
Assignment Scope: Assign at the Foundry Hub/Resource level.
Capabilities: They can manage virtual networks, customer-managed keys, and shared connections (e.g., Azure OpenAI) that all projects inherit.
[C]
Developer Permissions (Project Scope)
Developers should be restricted from changing the underlying resource configurations but need full access to their specific AI workloads.
Role: Azure AI Developer or Project Contributor.
Assignment Scope: Assign strictly at the Foundry Project level.
Capabilities: This allows them to create and update project assets (agents, flows, evaluations) and deploy models without permission to modify Hub-level infrastructure or security settings Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/rbac-foundry
NEW QUESTION # 125
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.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?
Answer: A
Explanation:
Enabling tracing via the prompt flow SDK alone does not guarantee that all four required dimensions - inputs, outputs, token usage, and latencies - are captured and surfaced in the Microsoft Foundry portal ' s trace viewer in a way that allows manual comparison across runs. While the prompt flow SDK does support tracing configuration, the portal-based Tracing activation is the complete, supported path for this specific capture requirement. The correct and complete solution as confirmed by the next question in this series is to use the Tracing toggle directly in the Microsoft Foundry portal for the project, then execute test runs. This portal-side Tracing feature is purpose-built to instrument all four dimensions and store them in a queryable trace store accessible through the Foundry interface.
Microsoft Learn Reference Topic: Enable tracing for prompt flows in Microsoft Foundry portal - Capturing run telemetry
NEW QUESTION # 126
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.
You manage a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
The application retrieves documents from an indexed knowledge base and generates answers for internal users.
Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
Solution: Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Review user feedback comments collected after deployment to determine whether answers appear more accurate.
Relying solely on user feedback comments is not the proper immediate action for evaluating this specific issue but it is still the best choice.
Evaluate whether your proposed change improves the Retrieval-Augmented Generation (RAG) system by reducing hallucinations (unsupported information), you need to measure faithfulness and context relevance using automated, quantifiable metrics.
The proper course of action is to implement an LLM-as-a-Judge framework using an open-source evaluation library like Ragas or TruLens.
Recommended Evaluation Plan
Establish a baseline: Run your current RAG pipeline through a test dataset of 50-100 representative user queries.
Capture the outputs: Save the user query, the exact retrieved document snippets, and the generated response for every test.
Apply the change: Deploy your proposed modification (e.g., altered prompt, different temperature, or re-ranking algorithm).Run the evaluation: Pass the test dataset through the updated pipeline to generate a new set of responses.
Compare the metrics: Use the framework to score both sets of data and mathematically verify if the change reduced unsupported claims.
Incorrect:
* Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Comparing embedding vector dimensions is not a valid or effective method for measuring RAG answer quality. Vector dimensions (e.g., 1536 or 3072) are static architectural properties of your embedding model. They do not reflect factual accuracy, semantic grounding, or the rate of hallucinations in your text generation.
* Measure token throughput and average response latency before and after applying the proposed change.
Measuring token throughput and latency is not the correct action to solve this specific problem.
Reference:
https://www.getmaxim.ai/articles/how-to-evaluate-your-rag-system/
NEW QUESTION # 127
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