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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language - Implement question answering and conversational AI |
| Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Implement document intelligence solutions - Enrich and index data for search scenarios |
| Plan and Manage an Azure AI Solution | - Select appropriate Azure AI services - Manage security, compliance, and responsible AI - Plan solution requirements and architecture |
| Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Implement custom vision models - Analyze images and videos using Azure AI Vision |
| Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions |
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NEW QUESTION # 322
You are building a chatbot by using the Microsoft Bot Framework SDK. The bot will be used to accept food orders from customers and allow the customers to customize each food item.
You need to configure the bot to ask the user for additional input based on the type of item ordered. The solution must minimize development effort.
Which two types of dialogs should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
https://learn.microsoft.com/en-us/azure/bot-service/bot-builder-concept-dialog?view=azure-bot- service-4.0#dialog-types
NEW QUESTION # 323
You have an Azure subscription.
You need to build an app that will compare documents for semantic similarity. The solution must meet the following requirements:
* Return numeric vectors that represent the tokens of each document.
* Minimize development effort.
Which Azure OpenAI model should you use?
Answer: C
Explanation:
Analysis:
* GPT-3.5 (A): A generative model; not specialized for embeddings # #
* Embeddings (B): Purpose-built models (like text-embedding-ada-002) return numeric vector representations of text, ideal for similarity comparisons # #
* DALL-E (C): Used for image generation, not semantic similarity # #
* GPT-4 (D): Powerful LLM, but not efficient for embeddings tasks # #
The answer:
B). embeddings
Reference:
Azure OpenAI embeddings
NEW QUESTION # 324
You are building a chatbot.
You need to configure the chatbot to query a knowledge base.
Which dialog class should you use?
Answer: B
Explanation:
* If a chatbot must query a knowledge base (KB), the Bot Framework uses QnAMakerDialog (inherited into Azure Cognitive Service for Language's Question Answering).
* AdaptiveDialog manages complex dialog flows but not specifically KB querying.
* ComponentDialog organizes dialogs, but again, not specific for knowledge bases.
* SkillDialog is used when invoking another bot (skill), not for KB queries.
Comprehensive Detailed ExplanationCorrect Answer: B. QnAMakerDialog
Reference: Use QnAMakerDialog in Bot Framework
NEW QUESTION # 325
You are building a Language Understanding model for an e-commerce chatbot. Users can speak or type their billing address when prompted by the chatbot.
You need to construct an entity to capture billing addresses.
Which entity type should you use?
Answer: B
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-entity-types ML Entity with Structure An ML entity can be composed of smaller sub-entities, each of which can have its own properties. For example, Address could have the following structure:
Address: 4567 Main Street, NY, 98052, USA
Building Number: 4567
Street Name: Main Street
State: NY
Zip Code: 98052
Country: USA
NEW QUESTION # 326
You have 100 chatbots that each has its own Language Understanding model.
Frequently, you must add the same phrases to each model.
You need to programmatically update the Language Understanding models to include the new phrases.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/client-libraries-rest-api
NEW QUESTION # 327
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