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| Section | Weight | Objectives |
|---|---|---|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| 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 # 154
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
- Ensure that background objects can be removed by applying a mask-
based inpainting edit.
- Preserve the original lighting and style of the edited images.
- Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
Answer: D
NEW QUESTION # 155
A customer support agent must answer from an internal knowledge base that is updated weekly with new product documentation, and its answers must always reflect the latest content. Which approach should you implement?
Answer: B
Explanation:
RAG injects fresh, frequently changing knowledge at inference time by retrieving relevant content from an index and grounding the model's answer in it, which is exactly the "chat with your data" pattern this scenario describes. Microsoft's guidance is direct on the boundary between the two techniques.
Use fine-tuning when you need to change model behavior, style, or task performance, rather than add fresh knowledge.
NEW QUESTION # 156
You have an Azure subscription that contains an Azure OpenAI resource named AI1.
You build a chatbot that uses AI1 to provide generative answers to specific questions.
You need to ensure that questions intended to circumvent built-in safety features are blocked.
Which Azure AI Content Safety feature should you implement?
Answer: D
Explanation:
Prompt Shields in Azure AI Content Safety are specifically designed to detect and block prompts or questions that attempt to bypass safety features in generative AI models, such as those used in chatbots. This feature ensures that the chatbot adheres to safety and ethical guidelines by preventing inappropriate or harmful content generation.
NEW QUESTION # 157
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 # 158
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:
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 # 159
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