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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
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NEW QUESTION # 40
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the temperatureparameter.
Does this meet the goal?
Answer: B
Explanation:
Correct:
* You add a reflection pass that regenerates the response if the required clauses are missing.
This is Self-Correction Strategy: A reflection pass allows an agent to evaluate its own initial output against specified constraints (e.g., checking for the presence of mandatory regulatory clauses). If the required text is missing, the agent triggers a programmatic self-correction or regeneration loop to include them before final delivery.
Incorrect:
* You increase the value of the max_tokens parameter.
Increasing the max_tokens parameter prevents the response from being cut off mid-sentence due to length constraints. However, it does not force the model's logic to explicitly include missing information that it chose to leave out earlier in the text.
* You increase the value of the temperature parameter.
Raising the temperature parameter increases randomness and creativity. For rigid compliance tasks like summarizing regulatory documents, higher temperature actually increases the risk of hallucination and omission.
* You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Evaluation Flow Block: Running an evaluation flow to score completeness and blocking bad responses identifies and stops low-quality outputs, but it does not fix or actively improve the response completeness. It simply filters failures out of the system.
Reference:
https://pub.towardsai.net/reflection-with-llm-how-to-make-ai-review-its-own-work-2db122fca1d8
NEW QUESTION # 41
You have a Microsoft Foundry project named Project1.
Project1 contains an application that processes PDF vendor invoices.
You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort.
What should you do?
Answer: A
Explanation:
Setting the output format parameter to Markdown is the correct and recommended action, but the exact property and enum name depend on whether you are interacting with the REST API directly or using the Python SDK.
To process PDF invoices and preserve their tables, headings, and visual sections in GitHub Flavored Markdown (GFM), configure your Azure Document Intelligence layout model parameters using the precise syntax detailed below.
Implementation Details
Python SDK Syntax: In the Azure Python client library, the parameter name is output_content_format, and its required value is DocumentContentFormat.MARKDOWN (rather than ContentFormat.MARKDOWN, which is used in the .NET C# SDK) Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/markdown-elements
NEW QUESTION # 42
You have a Microsoft Foundry project that contains a Retrieval Augmented Generation (RAG) chat solution used by customer support agents.
You are adding an automated pre-production evaluation step to a CI/CD pipeline named Pipeline1. The evaluation will run against a labeled test dataset that contains support questions and the expected grounding context.
You need to ensure that Pipeline1 fails if unsupported content or a retrieval mismatch exceeds a defined threshold:
- responses include claims not supported by the retrieved source
content
- retrieved source content does not align with the labeled expected
context
Which two built-in evaluators should you use in Pipeline1? Each correct answer presents pat of the solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
The Groundedness Evaluator evaluator validates that the model's responses include only claims supported by the retrieved source content It flags ungrounded content or hallucinations. If the average score drops below your defined threshold, it triggers a pipeline failure.
The correct additional built-in evaluator appropriate for the pipeline is Retrieval (specifically, the RetrievalEvaluator or DocumentRetrievalEvaluator).
A standard RAG evaluation pipeline assesses both the generator (the LLM producing the answer) and the retriever (the search system pulling documentation). The CI/CD requirements specify two distinct failure thresholds:
Responses including claims not supported by the retrieved source content: This checks for model hallucinations and is handled by the Groundedness Evaluator.
Retrieved source content not aligning with the labeled expected context: This explicitly measures the performance of your search step against your ground-truth data. The built-in Retrieval evaluator maps to this requirement. It computes metrics like context recall to ensure your system successfully retrieves the exact reference documents specified in your labeled test dataset Reference:
https://learn.microsoft.com/en-us/microsoft-365/copilot/extensibility/evaluations-cli-evaluators
NEW QUESTION # 43
You have a Microsoft Foundry project that contains an agent. The agent uses two tools to perform the following actions:
- Use Azure AI Search to retrieve answers from a private product
documentation index.
- Use the web search tool to retrieve public information on the
internet.
You need to ensure that for a specific run, the agent deterministically retrieves information only from the internet.
To what should you set tool_choice?
Answer: D
Explanation:
To force a specific run to exclusively use public information from the internet instead of your private documents, override the tool orchestration payload at the run level. Set the tool_choice directive specifically to the web grounding tool (e.g., bing_grounding or web_search) and constrain the tools array for that run.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search
NEW QUESTION # 44
Drag and Drop Question
You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 45
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