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| Section | Weight | Objectives |
|---|---|---|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
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NEW QUESTION # 72
You use Sentiment Analysis results from customer surveys to calculate bonuses for customer-service staff.
What should you do to meet Microsoft responsible AI principles?
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Add a human review and approval step before an AI-derived result affects an employee ' s financial outcome.
Sentiment analysis is probabilistic. It can be affected by ambiguity, sarcasm, domain-specific phrasing, language variation, and contextual information not present in the survey text. Using the model ' s output as the sole determinant of compensation could therefore produce unfair or insufficiently accountable results.
Human review provides an oversight mechanism in which the AI signal is treated as evidence rather than an unquestionable final decision. The reviewer can consider model confidence, additional performance information, relevant employment policies, and exceptional circumstances.
Including low-confidence predictions would increase uncertainty rather than mitigate it. Using information that should have been deleted creates data-governance and privacy concerns. Publishing raw customer surveys could expose customer information and does not improve responsible decision-making.
The principle is especially important for decisions with meaningful financial, employment, safety, or legal consequences.
The AI-103 Study Guide covers oversight modes, constraints, approval workflows, responsible AI instrumentation, and safety/governance controls , all of which support maintaining appropriate human accountability. Microsoft Learn
NEW QUESTION # 73
You have a Microsoft Foundry project that contains three agents as shown in the following table.
Name
Description
TriageAgent
Classifies incoming customer requests
PolicyAgent
Answers policy questions by searching internal content
ActionAgent
Creates or updates tickets by calling an HTTP API
You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
* Support a deterministic, step-based process that uses conditional branching and shared state across the agents.
* Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?
Answer: B
Explanation:
The correct answer is a workflow . Microsoft Foundry workflows are designed to orchestrate agents and business logic as declarative, predefined sequences of actions. The official workflow guidance states that workflows are ideal when you need to orchestrate multiple agents in a repeatable process, add branching logic such as if/else, and handle variables without writing application orchestration code. This directly matches the requirement for a deterministic, step-based process with conditional branching and shared state.
In this scenario, TriageAgent can classify the request first, the workflow can store the triage result, and conditional logic can determine whether to invoke PolicyAgent, ActionAgent, or both. The ticket action is optional, so it should be triggered through a workflow condition based on the triage output. This minimizes development effort because the branching, sequencing, and variable handling are managed in the Foundry workflow rather than being manually implemented across separate runs in application code.
A group chat session is better for dynamic agent handoff, not a strict deterministic process. Threads and runs or separate app-coordinated calls require more custom orchestration. Reference topics: Microsoft Foundry workflows, multi-agent orchestration, conditional branching, variable handling, and agent-driven workflows.
NEW QUESTION # 74
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
temperature = 0
output_config = { " effort " : " high " }
The correct configuration is temperature = 0 and output_config = { " effort " : " high " }. The requirement is to reduce small wording variations that are breaking automated validation. In chat completion requests, temperature controls sampling randomness. Microsoft's Azure OpenAI reference states that temperature ranges between 0 and 2, and that higher values make output more random while lower values make output more focused and deterministic. Therefore, the most stable setting from the available choices is 0, because it minimizes randomness and improves repeatability for validation-sensitive response patterns.
The solution must also maximize reasoning quality. The code already enables thinking with thinking={ " type
" : " enabled " }, so the remaining reasoning-quality control is the effort setting. Microsoft Foundry model guidance states that the effort parameter controls the quality/cost tradeoff and supports low, medium, and high effort levels. Selecting " high " maximizes reasoning quality among the available options.
Using temperature values of 1 or 2 would increase variability and make validation mismatches more likely.
Selecting low or medium effort would not meet the requirement to maximize reasoning quality. Reference topics: Microsoft Foundry model inference, chat model parameters, temperature, thinking, effort, and output stability.
NEW QUESTION # 75
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 run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Does this meet the goal?
Answer: A
Explanation:
The solution does not meet the goal. A completeness evaluation flow is useful for detecting incomplete responses, but detection and blocking do not improve the response itself. Microsoft Foundry RAG evaluators define Response Completeness as a metric that measures whether a response covers all critical information from the expected response or ground truth. It is a system evaluation signal used to assess response quality and produce pass/fail or scored results.
In this scenario, the issue is that the agent omits required regulatory clauses even though the clauses are present in retrieved content. Blocking low-scoring responses would prevent incomplete answers from being returned, but it would not revise the summary, add the missing clauses, or improve the generation process.
The appropriate improvement is to add a response-generation control such as a reflection or verification pass that checks the draft summary against the retrieved policy content and regenerates or amends the answer before returning it. Evaluation can support the quality gate, but by itself it is an assessment mechanism, not a completeness-enhancement mechanism. Reference topics: Microsoft Foundry RAG evaluators, response completeness, grounded generation, reflection, and response quality optimization.
NEW QUESTION # 76
You process vendor onboarding PDFs containing tables and hyperlinks. The extracted content is indexed and supplied to an agent in Markdown.
You need Markdown with layout and semantic structure optimized for RAG.
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use prebuilt-documentSearch . Microsoft ' s current Content Understanding documentation explicitly classifies prebuilt-documentSearch as a Retrieval Augmented Generation analyzer .
RAG analyzers extract content with layout represented as Markdown and perform semantic analysis to enhance retrieval quality for downstream applications. prebuilt- documentSearch extracts document elements including paragraphs, tables, and figures and produces layout-preserving structured Markdown optimized for indexing and retrieval. Microsoft Learn prebuilt-layout is useful when the principal requirement is document-layout extraction, but the question explicitly requires both layout and semantic structure optimized for RAG . That wording maps directly to the RAG analyzer rather than the base layout analyzer.
prebuilt-read primarily addresses basic textual extraction, while prebuilt-documentFieldSchema serves field
/schema-oriented scenarios rather than producing RAG-optimized document representations.
Therefore, although the supplied file displays C (prebuilt-layout) , the verified current Microsoft Learn answer is B (prebuilt-documentSearch) . Microsoft ' s SDK documentation also describes prebuilt- documentSearch as preserving layout, detecting tables and figures, and generating structured Markdown specifically for RAG. Microsoft Learn
NEW QUESTION # 77
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