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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
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NEW QUESTION # 117
You have an Azure subscription that contains a Microsoft Foundry resource. You need to build an app that will suggest product names from a given product description. Which Foundry model should you use?
Answer: D
Explanation:
Use GPT-4 because suggesting product names from a textual description is a natural-language generation task. The application can supply the product's characteristics, intended audience, tone, and naming constraints in a prompt, and GPT-4 can generate candidate names that reflect those requirements. GPT models interpret natural-language instructions and generate corresponding textual completions, making them appropriate for ideation, summarization, content drafting, and conversational applications.
The other model categories serve different modalities. DALL-E is an image-generation model that creates visual content from natural-language prompts; it is not intended primarily to produce product-name lists.
Whisper is a speech-recognition model used to transcribe audio into text. Embedding models convert text into numerical vectors for similarity search, clustering, classification, and retrieval scenarios; they do not generate human-readable product names. Microsoft's Foundry model documentation distinguishes chat and generative models from image-generation, audio, and embedding model families.
The app should therefore deploy GPT-4, construct a prompt containing the product description and naming criteria, and consume the generated textual response through the model inference endpoint.
Study Guide alignment: Select and deploy appropriate generative models, integrate deployed models into applications, and implement prompt-driven content-generation workflows.
NEW QUESTION # 118
Drag and Drop Question
You have a Microsoft Foundry project that contains a customer support agent grounded in internal documentation.
After a recent update, users report the following issues:
- Some answers are unsupported by retrieved documents.
- A small number of responses are flagged for policy violations.
You need to evaluate each issue.
Which observability signals should you use for each issue? To answer, drag the appropriate observability signals to the correct issues. Each observability signal may be used once, more than once, or not at all. You may need to drag the spit bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 119
Drag and Drop Question
You have a Microsoft Foundry project that contains an agent.
You need to enable long-term memory to ensure that the agent can recall user preferences across separate conversations. Stored memories must be isolated per authenticated user without the client application manually generating user IDs.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: "{{$userId}}"
User Isolation (Scope)
Setting scope="{{$userId}}" instructs the Microsoft Foundry Agent Service to automatically map and partition memory to the logged-in individual using their Microsoft Entra user identity template.
The platform handles this dynamically from request headers, removing the need for manual client-side user ID management Box 2: [memory_tool] Tool List: The tools configuration parameter expects an iterable containing instantiated tool objects. Wrapping memory_tool inside a Python list ([memory_tool]) exposes the capability to the agent definition.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/memory-usage?pivots=python
NEW QUESTION # 120
You have a Python application that sends customer support transcripts to Azure Language in Foundry Tools by using the REST API. The transcripts are in English and can contain customer names, email addresses, and phone numbers.
You need to store a redacted transcript of each conversation. The support workflow must redact only customer names and leave other entity types unchanged.
How should you complete the analyze-text request body? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The operation must use PII Entity Recognition because the required output is a redacted version of the input based on personally identifiable information.
The " kind " property should therefore be set to " PiiEntityRecognition " . The piiCategories parameter restricts which sensitive categories participate in the analysis. Setting it to only [ " Person " ] ensures that recognized customer names are selected while other PII categories-such as PhoneNumber and email-related categories-are not included in the requested PII category set.
This distinction is important because the service supports numerous PII entity categories. Without category restriction, additional sensitive information could be recognized and redacted, which would violate the requirement to leave email addresses and phone numbers unchanged.
EntityRecognition is general Named Entity Recognition and is designed to identify broad entities such as Person, Organization, Location, and DateTime; it is not the specialized redaction operation required here.
LanguageDetection only identifies language.
The resulting PII response can provide redacted text and entity metadata that the application stores or audits.
Study Guide alignment: configure detection of sensitive content and implement solutions that extract entities and structured information from text . Microsoft Learn
NEW QUESTION # 121
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
Answer: D
Explanation:
To suggest product names from a given product description, the ideal model is GPT-4. GPT-4 is a powerful language model that can understand and generate human-like text based on given inputs, making it well-suited for tasks such as generating creative suggestions like product names from descriptions.
Whisper is used for speech-to-text conversion, not for generating text suggestions.
DALL-E is designed for generating images from text prompts, not for text generation tasks like naming products.
Embeddings are used for tasks involving similarity search and semantic understanding but not for generating new text like product names.
NEW QUESTION # 122
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