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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 2: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 3: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 4: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 5: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
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NEW QUESTION # 89
You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Identify each presenter in the video and attribute each text file to
sentences
- Preserves the original sentence order
- Returns exactly three sentences
Which Language service feature should you use?
Answer: C
Explanation:
The Azure AI Language service's extractive summarization feature is the appropriate choice to summarize video transcripts while preserving original sentence structure and order. Using the REST API or client library, you can set sentenceCount to exactly three to generate a concise summary from key phrases.
This approach avoids generating new, abstract text, fulfilling the constraint to keep original text, unlike abstractive summarization.
Key Implementation Details:
Service: Azure AI Language, specifically the Summarization feature.
Method: Extractive summarization selects the most relevant sentences directly from the source text, which is ideal for maintaining the original context, speaker attribution, and sentence structure.
Configuration: The API call allows setting the sentenceCount parameter to three, ensuring the output is limited to that exact number.
Workflow: This model works best on conversational transcripts processed via speech-to-text (e.g., Azure AI Speech), where speakers are identified, allowing the summarization tool to aggregate sentences while preserving the speaker-to-text relationship.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/text-summarization
NEW QUESTION # 90
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 91
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: A
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 # 92
You have a Microsoft Foundry project that contains an agent for a customer support chat app.
The agent uses a memory store and a memory search tool.
You need to ensure that the conversation history does NOT persist across separate sessions.
To what should you set the scope of the memory tool?
Answer: B
Explanation:
To ensure that conversation history does not persist across separate sessions, you must set the scope to session.
The session scope restricts data access to the current active chat instance. It automatically wipes or ignores previous data when a new session starts.
Incorrect:
[Not C]
user or {{$userId}} scope: Persists data across multiple sessions for that specific user. This would cause the exact issue you want to avoid by carrying historical context into new conversations.
References:
https://ai.gopubby.com/agents-with-memory-conceptual-undestanding-part-01-f6caedfcd96d?gi=a9aa95df15c6
NEW QUESTION # 93
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
Answer: B
Explanation:
To prevent potentially malicious instructions embedded within untrusted external sources (such as text extracted from uploaded images via OCR) from being processed by the model, you should use Prompt Shields for Documents (specifically designed for Indirect Prompt Attacks or Cross- Domain Prompt Injections).
Key Features to Implement
Prompt Shields for Documents: This specific component of Azure AI Content Safety / Prompt Shields analyzes external data--like text pulled from files, emails, or OCR outputs--appended to the prompt. It identifies and neutralizes hidden adversarial instructions trying to trick the model into overriding its system protocols.
Spotlighting: A native capability within Microsoft Foundry's Prompt Shields, Spotlighting uses advanced text-formatting techniques to separate valid user commands from the untrusted document text. This prevents the model from mistaking malicious instructions embedded inside the OCR output for instructions from the developer or end-user.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/azure-ai-announces-prompt-shields-ga/4236033
NEW QUESTION # 94
......
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