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| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Topic 2: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 3: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 4: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Topic 5: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
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NEW QUESTION # 62
You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of each file that can be uploaded to the service?
Answer: C
Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/agents/quotas-limits
NEW QUESTION # 63
You need to configure Agent1 to meet the security and compliance requirements.
What should you use?
Answer: B
NEW QUESTION # 64
You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
Answer: C
NEW QUESTION # 65
You have a Microsoft Foundry project that serves a high-volume chat app.
Most requests are simple FAQs, but some require advanced reasoning.
You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions.
What should you do?
Answer: B
Explanation:
The correct choice is to use a model cascade that routes the requests to different models . In Microsoft Foundry, this pattern aligns with model routing: simple, low-risk prompts can be handled by smaller, faster, lower-cost models, while complex prompts can be escalated to more capable or reasoning models. Microsoft's Foundry model router guidance states that the router optimizes cost and latency while maintaining comparable quality by using smaller, cheaper models when they are sufficient and larger or reasoning models when the task requires more advanced capability.
This directly matches the scenario: most traffic consists of simple FAQs, so routing those requests to efficient models reduces average latency and token-processing cost. Advanced reasoning requests still receive high- quality responses because they are routed to models with stronger reasoning capability. Microsoft's model router documentation also explains that routing decisions consider prompt difficulty, cost, quality, latency, and conversation context, making it suitable for diverse chat workloads.
Increasing max_tokens for all requests would usually increase cost and latency. Sending all requests to a smaller model risks poor quality for complex questions, while sending all requests to the most capable model wastes cost and latency on simple FAQs. Reference topics: Microsoft Foundry model routing, model selection, generative AI optimization, latency management, and cost-aware AI application design.
NEW QUESTION # 66
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 increase the value of the temperature parameter.
Does this meet the goal?
Answer: B
Explanation:
The solution does not meet the goal. Increasing temperature changes the sampling behavior of the generative model, not the completeness-checking logic of the application. Microsoft's Azure OpenAI reference defines temperature as a sampling control where higher values make output more random, while lower values make output more focused and deterministic. Raising the value can increase variation and creativity, but it does not ensure that all required regulatory clauses from the retrieved policy documents are included.
The reported issue is a recall/completeness failure: relevant clauses are already present in retrieved content, but the generated summary omits them. Microsoft Foundry RAG evaluator guidance defines Response Completeness as whether a response covers critical information compared to expected information or ground truth, and distinguishes it from groundedness, which checks that responses do not go beyond grounding context.
A more suitable implementation would add a reflection, verification, or completeness review pass that compares the draft summary against the retrieved clauses and revises the response before returning it.
Increasing temperature could make outputs less predictable and may worsen omission risk. Reference topics:
model parameters, temperature, RAG response completeness, retrieved context, and model reflection.
NEW QUESTION # 67
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