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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 2: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 3: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 4: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 5: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
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問題 #131
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 AI 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?
答案：A
解題說明：
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
問題 #132
You need to configure an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
答案：C,F
解題說明：
The correct built-in skills are Azure OpenAI Embedding and Text Split. The case study requires an indexing pipeline that enables semantic and vector search over the product sheets stored in Azure Blob Storage, so Agent1 can retrieve relevant product information for natural language customer questions. For a RAG pipeline, long PDF content must first be broken into retrievable chunks, and each chunk must then be vectorized for semantic similarity retrieval.
Microsoft's Azure AI Search integrated vectorization guidance states that you create a skillset that calls the Text Split skill for chunking and the Azure OpenAI Embedding skill to vectorize the chunks. The Text Split skill breaks text into chunks and provides positional metadata, making it suitable when downstream embedding skills have input-length limits. The Azure OpenAI Embedding skill connects to an embedding model deployed in Azure OpenAI or a Microsoft Foundry project and generates embeddings during indexing.
Language Detection, Entity Recognition, and key phrase extraction can enrich text, but they do not create vector embeddings. Merge is useful for combining OCR text with document text, but it does not satisfy the core vector-search requirement. Reference topics: Azure AI Search skillsets, Text Split skill, Azure OpenAI Embedding skill, integrated vectorization, and RAG indexing.
問題 #133
You have 100,000 images.
You need to build an app that will perform the following actions:
* Identify road signs in the images and extract the text on the signs.
* Analyze the text to identify well-known locations.
The solution must minimize development effort.
答案：
解題說明：
Explanation:
* Extract the text: Azure Vision in Foundry Tools
* Identify well-known locations: Azure Language in Foundry Tools
Azure Vision in Foundry Tools is the appropriate service for extracting text from road signs. Its pretrained Optical Character Recognition capability detects printed or handwritten text in images and returns recognized words, lines, and their coordinates. Microsoft specifically identifies street signs as an applicable OCR scenario. Because the OCR model is already trained, it avoids the data labeling and custom-model training that would increase development effort.
After OCR produces the text, Azure Language in Foundry Tools can analyze it through entity linking . Entity linking recognizes and disambiguates well-known entities and connects them to entries in a recognized knowledge base. This is suitable for determining that text on a sign represents a known geographic location rather than merely returning an unclassified text string. Named entity recognition can also classify extracted entities under categories such as Location, but entity linking provides the stronger interpretation implied by
"well-known locations."
Azure Document Intelligence is optimized for structured and text-heavy business documents, while Azure AI Search provides indexing and retrieval rather than primary OCR and linguistic analysis.
Study Guide alignment: implement OCR-based ingestion and use Foundry Tools to extract entities from text .
問題 #134
You have a Python application that uses Azure OpenAt structured outputs to extract fields from unstructured receipt text and support ticket text. The application uses the following schema.
答案：
解題說明：
Explanation:
* The schema requires that a receipt has a discount_code value - No
* The schema can be used as is for Azure OpenAI structured outputs - No
* The generated JSON object will preserve the property order merchant, order_number, discount_code, line_items, and total - Yes The discount_code definition permits a nullable result. Consequently, the output can contain " discount_code
" : null when the source receipt contains no discount code. The property can be structurally present without containing a substantive discount-code value; therefore, the first statement is No .
The schema cannot be submitted unchanged. Azure OpenAI structured outputs support only a defined subset of JSON Schema. Every property declared in an object must be included in that object's required array.
Optional information must instead be represented as a required property whose type includes null. Each object must also specify " additionalProperties " : false. Furthermore, although nested anyOf constructs are supported, an anyOf construct cannot be used as the root schema. A schema that violates any of these restrictions must be revised before use, making the second statement No .
The third statement is Yes . Azure OpenAI structured outputs preserve keys in the order in which the properties are defined in the supplied schema. Therefore, the receipt object will emit merchant, order_number, discount_code, line_items, and total in that sequence. This behavior supports predictable parsing and downstream integration. The relevant curriculum area is Use Azure OpenAI in Foundry Models to generate content , including application integration and structured model responses.
問題 #135
You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
- Some requests take more than 15 seconds to complete.
- Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information.
Which observability capability should you use?
答案：D
解題說明：
Here is LLM tracing (also known as trace view or distributed tracing for GenAI) needed.
Tracks Execution Flow: It captures the exact ordered sequence of LLM calls and tool invocations.
Pinpoints Latency: It provides timestamps and durations for every individual step to catch the 15- second bottlenecks.
Inspects Inputs/Outputs: It lets you see the exact payload sent to and from the knowledge API and the final LLM prompt to find out why the agent hallucinated or ignored the data.
Reference:
https://coralogix.com/ai-blog/advanced-techniques-for-monitoring-traces-in-ai-workflows/
問題 #136
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