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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| 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
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
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NEW QUESTION # 157
Drag and Drop Question
A team operates a generative AI-powered customer support assistant built on Microsoft Foundry.
The application serves users globally and supports both real-time chat interactions and batch summarization jobs.
The team must ensure that the application continues to meet defined service-level objectives (SLO) as usage increases.
The team requires visibility into runtime behavior to identify performance regressions that affect the user experience and system capacity.
You need to select the performance metrics that meet the requirements.
Which performance metric should you monitor for each requirement? To answer, move the appropriate performance metrics to the correct requirements. You may use each performance metric 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 # 158
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:
You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 159
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.
For each of the following statements, select Yes if the statement is true. Otherwise, select No . NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Preference comparison data with chosen versus rejected response pairs is the input format for Direct Preference Optimization (DPO) or RLHF-style fine-tuning - an advanced fine-tuning technique available in Microsoft Foundry. A valid DPO dataset record must have three fields: a prompt as the input, a chosen field containing the preferred response, and a rejected field containing the less preferred response. The file must be in JSONL format with UTF-8 encoding, where each line represents one complete preference pair. When evaluating statements about this dataset, mark True if the dataset contains all three required fields and chosen responses represent higher-quality outputs than rejected ones. Mark False if the format is incompatible with DPO requirements, if the required rejected field is missing, or if the chosen and rejected responses appear to be of equivalent quality with no clear preference signal.
Microsoft Learn Reference Topic: Advanced fine-tuning with preference data in Microsoft Foundry - DPO dataset format
NEW QUESTION # 160
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?
Answer: C
Explanation:
An appropriate base model for fine-tuning with text and image inputs in the Microsoft Foundry portal is GPT-4o (specifically versions like gpt-4o-2024-05-13 or newer). This model supports multi-modal training data, allowing you to improve performance on tasks requiring both visual understanding and textual response within Azure OpenAI Service.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-fine-tuning- capabilities-with-images-on-azure-openai-service/4303695
NEW QUESTION # 161
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: Review user feedback comments collected after deployment to determine whether answers appear more accurate.
Does the solution meet the goal?
Answer: B
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 # 162
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