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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 2: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 3: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
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NEW QUESTION # 79
You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?
Answer: D
Explanation:
A custom speech model improves recognition of specialised vocabulary, such as medicine names, by adapting speech-to-text (STT) to your domain, and AI-103 covers integrating speech, including custom speech models, as an agent modality. This directly addresses the accuracy problem in the scenario.
NEW QUESTION # 80
You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
* Complex questions must retrieve information from multiple chunks.
* Multi-turn conversations must influence retrieval planning.
* Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?
Answer: A
Explanation:
The correct answer is agentic Retrieval Augmented Generation (RAG) because the requirements describe the agentic retrieval pipeline in Azure AI Search. Agentic retrieval is designed for chat and copilot scenarios where a user's request can be complex, conversational, and dependent on prior turns. Azure AI Search agentic retrieval uses an LLM-assisted planning stage to break a complex request into focused subqueries, allowing the system to retrieve grounding information from multiple chunks rather than relying on a single query path.
Microsoft's Azure AI Search guidance describes agentic retrieval as a multi-query pipeline for complex questions in chat and agent workflows, with subqueries that can include chat history for additional context.
This also satisfies the latency requirement because agentic retrieval runs the generated subqueries in parallel and then merges and reranks the best results for use by the generative model. Classic RAG is simpler and typically sends a single query to search, making it less suitable for multi-hop or conversational retrieval planning. Chain of thought is a reasoning technique, not an Azure AI Search retrieval approach, and iterative retrieval does not specifically provide the built-in query planning, conversation-aware retrieval, and parallel execution described here. Reference topics: Azure AI Search agentic retrieval, RAG with Azure AI Search, knowledge bases, query planning, and generative AI grounding.
NEW QUESTION # 81
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAI model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?
Answer: B
Explanation:
To handle HTTP 429 throttling and improve load testing reliability for your Microsoft Foundry Agent Service, implement exponential backoff with jitter on the client side, deploy a load- balancing gateway across multiple Azure OpenAI regions, or upgrade your deployment to Provisioned Throughput Units (PTU).
*-> 1. Implement Client-Side Retry LogicConfigure your agent's HTTP client or SDK to handle
429 errors gracefully rather than failing immediately.
Exponential Backoff: Increase the wait time between subsequent retry attempts exponentially .
Jitter: Add a small amount of random delay (jitter) to the backoff time to prevent a "thundering herd" effect where all throttled requests retry simultaneously.
Header Inspection: Programmatically read the Retry-After or x-ratelimit-reset values from the HTTP 429 response headers to pause execution for the exact duration requested by Azure.
2. Set Up Multi-Region Load Balancing
3. Switch to Provisioned Throughput (PTU)
4. Optimize Token Consumption
Reference:
https://learn.microsoft.com/en-us/answers/questions/1518859/help-with-resolving-ptu-m-service-429-error
NEW QUESTION # 82
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: A
Explanation:
The correct answer is D. prompt shields for documents . The OCR text is extracted from uploaded images and appended as additional context, so it is third-party content rather than a direct trusted user instruction.
Microsoft Foundry Prompt Shields distinguish between user prompt attacks , which are malicious instructions directly supplied as user prompts, and document attacks , which are hidden or embedded instructions in third-party content such as documents, emails, webpages, or grounded data. Microsoft also notes that Prompt Shields analyze indirect attacks embedded in input documents or images.
This scenario is an indirect prompt injection pattern: an uploaded screenshot may contain text that says to ignore the system prompt or perform unauthorized actions. Because the OCR output is being passed to the multimodal model as contextual content, the appropriate protection is Prompt Shields for documents, which scans externally sourced context for malicious embedded instructions before generation. Protected material text detects known copyrighted text in model outputs, not prompt injection. Image moderation detects harmful visual content categories, not malicious instructions in extracted context. Prompt Shields for user prompts are less precise here because the risk comes from document-like content derived from the uploaded image.
Reference topics: Prompt Shields, document attacks, indirect prompt injection, multimodal safety, OCR- derived context, and Foundry guardrails.
NEW QUESTION # 83
You have a Microsoft Foundry project that contains two agents named PolicyWriter and RiskReviewer.
PolicyWriter generates draft updates for customer policies, and RiskReviewer reviews the drafts. In the visual builder, you need to create a workflow that meets the following requirements:
* Finalizes low-risk updates without manual intervention
* Ensures predictable execution across the agents
Answer:
Explanation:
Explanation:
* Orchestration pattern: The sequential template that passes outputs node-by-node
* Approval checkpoints: Add a condition statement
Use the sequential orchestration template because the agents must execute in a fixed, predictable order.
PolicyWriter first generates the proposed policy update, and its output is then passed directly to RiskReviewer. Microsoft defines the sequential pattern as passing the result from one agent to the next in a defined order, making it appropriate for deterministic, multistage processing. The group-chat pattern would allow control to move dynamically between agents, which would reduce execution predictability.
After RiskReviewer returns a structured risk classification, add a condition statement that evaluates the result. The low-risk branch can proceed directly to the finalization action without requesting human input. A separate higher-risk branch can route to an approval step, such as an Ask a question node. Adding an Ask a question node without conditional branching would pause every workflow execution, including low-risk updates, and therefore would not satisfy the automation requirement. Microsoft Foundry workflows support if
/else branching and condition expressions for selecting the next action.
Study Guide alignment: implement orchestrated multi-agent solutions and build autonomous or semiautonomous workflows with safeguards and approval-flow controls .
NEW QUESTION # 84
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