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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 | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Topic 4: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Topic 5: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
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NEW QUESTION # 90
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 # 91
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 image moderation to block unsafe content before processing the images.
Does this meet the goal?
Answer: A
Explanation:
The solution does not fully meet the goal. Image moderation is appropriate for one part of the risk: blocking unsafe image content before the image is processed. Azure AI Content Safety provides image APIs that detect harmful content, and its harm categories and severity levels can be used to classify and block objectionable image content. This addresses unsafe photos, but it does not address hidden instructions embedded in images.
The second risk is prompt manipulation through extracted image text. After OCR extracts text from the uploaded image, that text becomes untrusted third-party content supplied to a generative model. Microsoft defines document attacks as malicious instructions embedded in third-party content, where the objective is to cause the model to execute unintended commands or alter intended behavior. Prompt Shields are the control designed to detect user prompt attacks and document attacks, including indirect attacks that come from uploaded or referenced content.
Therefore, image moderation alone is incomplete. A complete mitigation would combine image moderation for harmful visual content with Prompt Shields for document attacks, and optionally Spotlighting, so extracted or embedded text is treated as lower trust. Reference topics: Azure AI Content Safety, image moderation, Prompt Shields, document attacks, indirect prompt injection, and multimodal safety.
NEW QUESTION # 92
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 # 93
You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
- Support turn-taking dynamics, where the agent begins to generate the
speech output before the user finishes speaking.
- Operate with low latency to maintain conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?
Answer: A
Explanation:
To achieve low latency and natural turn-taking dynamics in this specific Microsoft Foundry workflow, the best approach is to use real-time speech-to-text for incoming audio and text-to- speech for agent responses.
Low Latency: Streaming, real-time Speech-to-Text (STT) and Text-to-Speech (TTS) pipelines process audio chunks concurrently. This allows the system to analyze text and prepare responses while the user is still speaking.
Turn-Taking Support: Real-time STT systems utilize voice activity detection (VAD) and continuous streaming. This enables the agent to instantly detect pauses, interruptions, or trailing speech to naturally shift conversational turns.
Direct Reasoning Compatibility: Because your workflow requires converting input into text for reasoning (such as prompting a Large Language Model), a highly optimized text-based pipeline fits seamlessly without extra translation layers.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/priority-processing
NEW QUESTION # 94
Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1. What should you include in the recommendation?
Answer: D
Explanation:
The best choice to include to support this scenario is Azure AI Search.
Retrieval-Augmented Generation (RAG): Azure AI Search acts as the primary knowledge base and indexing infrastructure for AI agents. It ingests raw unstructured data directly from Azure Blob Storage, indexes it, and creates vector embeddings.
Natural Language Queries: It supports semantic ranking and vector search. This allows the Foundry agent to handle user questions written in natural language and find exact matches or conceptual relevance.
Relevance and Accuracy: By leveraging grounding data through an indexer, it ensures the agent's responses are strictly tied back to the product detail sheets, preventing hallucinations and verifying accuracy.
Incorrect:
[Not B]
Grounding with Bing Search: This tool connects the agent to the live internet. Because the product sheets are proprietary company data stored securely inside your private Azure Blob Storage account, Bing Search cannot access them and will not help answer specific questions about Contoso products.
[Not D]
Why Document Intelligence is Incorrect
Extraction vs. Retrieval: Azure Document Intelligence is used to extract structure, text, tables, and form fields from files to turn unstructured data into structured data formats.
Missing Search Infrastructure: It does not possess a native search indexer, vector database, or semantic query engine to accept natural language user questions and dynamically return grounded answers. It is instead used as a tool prior to or alongside Azure AI Search to help clean and structure highly complex layouts during the data ingestion pipeline.
Scenario:
Technical Requirements;
Responses generated by using the product sheet information must be relevant, complete, and accurate.
*-> Agent1 must be able to use the product sheets to answer natural language questions about product details.
Data environment: The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
Planned changes: Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
Reference:
https://pub.towardsai.net/build-a-rag-powered-ai-agent-with-microsoft-foundry-and-foundry-iq-via-the-azure-portal-7eb138b4ae8f
NEW QUESTION # 95
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