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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 2: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 3: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 4: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 5: Implement agentic solutions | 20-25% | - Manage agent operations
|
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NEW QUESTION # 28
You are developing a new sales system that will process user-generated video and text from a public-facing website.
You plan to notify users that their data has been processed by the sales system.
Which responsible AI principle does this help meet?
Answer: D
Explanation:
Notifying users that their data has been processed by your sales system fulfills the Transparency responsible AI principle. Transparency ensures that users are informed about how their data is collected and processed, which helps foster understanding and build trust between users and technology providers.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/responsible-ai
NEW QUESTION # 29
You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Summarizes documents that contain multiple sentences
- Preserves the original sentence order
- Returns exactly three sentences
Which Language service feature should you use?
Answer: C
Explanation:
E xtractive summarization within the Azure AI Language service is the appropriate feature for the application's requirements.
This feature can be configured to:
Return exactly three sentences by using the sentenceCount parameter (which defaults to 3). The supported range is from 1 to 20 sentences.
Preserve the original sentence order by specifying the sortBy parameter as Offset, which is the default behavior. The Offset value corresponds to the character position of the extracted sentences in the original document.
Reference:
https://docs.azure.cn/en-us/ai-services/language-service/summarization/how-to/text-summarization
NEW QUESTION # 30
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: A
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 # 31
You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements:
* Retain user preferences across multiple conversations.
* Enable users to provide contextual grounding by directly uploading documents during a chat.
Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To retain user preferences across conversations, use: Agent memory that uses persistent storage To enable users to provide contextual grounding during chats, use the: file search tool The correct capability for retaining user preferences is agent memory that uses persistent storage .
Microsoft Foundry Agent Service memory is a managed long-term memory capability that enables continuity across sessions, devices, and workflows. It is specifically intended to let agents retain user preferences, maintain relevant historical context, and personalize responses across separate conversations. Memory stores provide the persistent storage layer, and scope can be used to segment memories for secure user-specific experiences.
The correct capability for contextual grounding from user-uploaded documents is the file search tool .
Microsoft describes file search as the tool that enables Foundry agents to search through documents and retrieve relevant information from outside the base model, including proprietary product information and user- provided documents. The file search workflow supports uploading files, creating a vector store, enabling the tool on the agent, and querying those documents through the agent.
Conversation history alone supports continuity within a conversation, but it is not durable preference memory across multiple conversations. An Azure AI Search tool is better for preconfigured enterprise indexes, while file search is the direct document-upload grounding capability. Reference topics: Foundry Agent Service memory, memory stores, File Search tool, vector stores, and grounded agent responses.
NEW QUESTION # 32
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
NEW QUESTION # 33
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