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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 41
You need Pipeline1 to fail if:
* responses include claims not supported by retrieved source content;
* retrieved source content does not align with the labeled expected context.
Which two built-in evaluators should you use?
Answer: B,D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use Groundedness to detect claims in the generated answer that are not supported by the retrieved source context. Groundedness is fundamental to RAG evaluation because it determines whether the model stays faithful to the evidence supplied to it rather than introducing unsupported information.
Use the Retrieval evaluator to measure whether the search/retrieval stage returns content that aligns with the expected grounding information in the labeled dataset. A failure at this stage means the generation model might receive incomplete or irrelevant evidence even if it behaves correctly.
These two evaluators therefore inspect distinct parts of the RAG pipeline:
* Retrieval evaluates the evidence selected by the retrieval system.
* Groundedness evaluates whether the generated answer is supported by that evidence.
Response Completeness evaluates whether an answer sufficiently covers expected information. Fluency measures linguistic quality, and Coherence assesses organization and logical readability. None directly covers the two specified failure modes.
This combination is appropriate for a CI/CD quality gate because retrieval regressions and unsupported generation can be detected separately before production deployment.
The AI-103 Study Guide explicitly covers evaluating models and apps for fabrications, relevance, quality, and safety , together with monitoring grounding quality. Microsoft Learn
NEW QUESTION # 42
Hotspot Question
You develop a test method to verify the results retrieved from a call to the Azure Vision in Foundry Tools API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment:
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:
Box 1: Yes
The code segment correctly filters for and displays the name (and coordinates) of each detected brand only if the model's confidence score is 75 percent or higher.The expression if brand.confidence >= 0.75 guarantees that only brands meeting or exceeding this threshold are printed.
Box 2: Yes
The code segment will display the coordinates. Specifically, it prints the x and y values of the rectangle's top-left corner alongside its width (w) and height (h) for any detected brand with a confidence score equal to or greater than 0.75 (75%).
The provided code uses the properties directly to extract the bounding box:
brand.rectangle.x and brand.rectangle.y: The coordinates of the top-left corner of the bounding box.
brand.rectangle.w and brand.rectangle.h: The width and height of the bounding box.
Box 3: No
See Box 2 above.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection
NEW QUESTION # 43
You need to configure Agent1 to answer customer questions about only the Contoso products. The solution must meet the business requirements.
What should you do?
Answer: D
Explanation:
The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that the business requirement is for Agent1 to answer questions only about the products sold by Contoso . This is a behavioral boundary for the agent, so it should be implemented in the highest-priority instructions that define the agent's role, allowed scope, and refusal behavior.
Microsoft Foundry guidance states that a system message is used to steer model behavior, define the assistant' s role and boundaries, and add safety or quality constraints for the scenario. The system message should instruct Agent1 to answer only when the question concerns Contoso products, use the configured Contoso product documentation as grounding, and politely refuse or redirect questions about non-Contoso products.
Top-p sampling and temperature control randomness, not business-domain scope. Increasing temperature would make responses less deterministic. Few-shot examples can support desired behavior, but examples alone are weaker than explicit system-level instructions for defining operating boundaries. Reference topics:
system message design, prompt engineering, agent instructions, response constraints, and grounded generative AI behavior.
NEW QUESTION # 44
You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page- number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?
Answer: A
NEW QUESTION # 45
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 multimodal Al generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure a prompt shield for user prompts.
Does this meet the goal?
Answer: A
Explanation:
The solution does not meet the goal. Prompt Shields for user prompts are designed to detect direct attempts by a user to manipulate the model through the prompt itself. In this scenario, the malicious instructions are embedded inside uploaded images and then introduced into the model context through extracted image text.
That pattern is an indirect prompt injection or document attack, not merely a direct user-prompt attack.
Microsoft's Prompt Shields guidance distinguishes between user prompt attacks and document attacks, and states that document attacks involve harmful instructions embedded in supplied documents or third-party content.
The solution is also incomplete because users can upload unsafe images. Azure AI Content Safety includes image APIs that detect harmful content in images and support moderation across modalities. A complete mitigation would combine image moderation for unsafe visual content with Prompt Shields for document attacks, and optionally Spotlighting, so OCR-derived or embedded image text is treated as lower-trust context.
Prompt Shields for user prompts alone would not reliably block unsafe images or hidden instructions extracted from those images. Reference topics: Azure AI Content Safety, Prompt Shields, user prompt attacks, document attacks, image moderation, and multimodal safety.
NEW QUESTION # 46
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