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| Section | Objectives |
|---|---|
| Topic 1: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Topic 2: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 3: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
| Topic 4: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Topic 5: Develop Generative AI Applications and Agents | - AI agents architecture
|
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NEW QUESTION # 79
You have a Microsoft Foundry project that uses Azure AI Search to ground an agent in internal documentation.
After a recent content update, users report that the agent's answers have become less accurate.
You need to identify whether the retrieved content is negatively influencing the model's generated responses.
Which observability signal should you review?
Answer: D
Explanation:
The most interesting observability signal in this scenario is Retrieval Score vs. Response Quality (Groundedness Evaluation), specifically analyzing the Context Relevance metric alongside Retrieval Confidence Scores.
Because your LLM agent uses a Retrieval-Augmented Generation (RAG) pattern via Azure AI Search, a sudden drop in accuracy after a content update usually implies that the search index is either surface-matching bad keywords, or the ranking algorithm is pulling technically relevant but contextually wrong documents.
Reference:
https://blog.gopenai.com/how-to-prevent-context-loss-in-azure-ai-search-rag-pipelines-497c346c7077
NEW QUESTION # 80
You are creating an enrichment pipeline that will use Azure Al Search. The knowledge store contains unstructured JSON data and the text from scanned PDF documents.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use an object projection for the unstructured JSON data. Object projections store a JSON representation of an enrichment tree node in an Azure Blob Storage container. They preserve hierarchical fields and complex structures, making them appropriate when enriched document content must remain available as a complete JSON object rather than being decomposed into relational rows. Microsoft describes object projections as JSON representations that can be sourced from nodes in the enrichment tree.
For the scanned PDF content, use a file projection . During document cracking and OCR processing, scanned pages are represented through the /document/normalized_images/* collection. File projections write these binary normalized images to Blob Storage, preserving the page assets from which the text is extracted.
Microsoft specifies that file projections operate only on normalized images and contain binary data rather than JSON.
A table projection is intended for row-and-column structures used by analytical tools such as Power BI. It is not the appropriate choice for preserving hierarchical JSON or scanned-document image files.
Study Guide alignment: configure Azure AI Search enrichment pipelines, skillsets, knowledge stores, and table, object, and file projections .
NEW QUESTION # 81
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 # 82
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project. User messages can contain both text and images. Currently, App1 includes image URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
Answer: A
Explanation:
A vision-enabled Chat Completions request must represent the user message as a multimodal content array.
One item contains the prompt with " type " : " text " , while another contains the image reference with " type ": " image_url " and an image_url.url property:
" content " : [
{ " type " : " text " , " text " : " Analyze this image. " },
{ " type " : " image_url " , " image_url " : { " url " : " < image-url > " }}
]
This structure explicitly identifies each modality, allowing the deployed model to route the text and image through the appropriate processing paths. A URL embedded in an ordinary string remains text and is not interpreted as visual input. Microsoft's documented Chat Completions schema shows the text and image objects inside the user message's content array.
Request metadata does not define model input, and a system message should contain behavioral instructions rather than the user's image attachment. Base64 images are supported, but the encoded value must be formatted as a data URL and supplied through the structured image item-not inserted into a plain content string.
Study Guide alignment: deploy and consume multimodal models, integrate generative workflows into applications, and design multimodal-understanding workflows .
NEW QUESTION # 83
Drag and Drop Question
You have a Microsoft Foundry project that contains a multi-agent solution. The agents use tool calling to query internal systems.
You need to implement responsible AI auditing to meet the following requirements:
- Capture all the nested operations across the entire agent run.
- Record tool invocation arguments and retuned results as metadata.
What should you use for each requirement? To answer, drag the appropriate options to the correct targets Each option may be used once, more than once, or not at all. You may need o dag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Hierarchical spans
Hierarchical spans should be used in this case.
Parent-Child tracking: In OpenTelemetry and Azure Monitor Application Insights (which back Microsoft Foundry environments), hierarchical spans utilize a unique Trace ID for the entire request and distinct Span IDs for individual operations.
Nested capture: When a primary agent calls a sub-agent, or an agent invokes a specific system tool, each subsequent operation is recorded as a child span. This explicitly preserves the parent- child relationship, allowing auditors to reconstruct the exact execution tree of all nested operations across the entire multi-agent run Box 2: Tool call attributes To record tool invocation arguments and returned results as metadata for responsible AI auditing in a Microsoft Foundry multi-agent project, you should use Tool call attributes.
In GenAI and multi-agent systems leveraging OpenTelemetry semantic conventions (which Microsoft Foundry utilizes for its observability, tracing, and logging pillars), tool execution details are captured via specific span attributes. Configured tool call attributes explicitly map and record the parameters (tool.call.arguments) and outputs (tool.call.result) as metadata keys inside the tracing context for auditing and validation.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/observability/concepts/trace-data
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/ai-observability-starter-kit-for-microsoft-foundry-agents/4522751
NEW QUESTION # 84
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