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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Orchestrate multiple models and containers |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Build knowledge bases and search indexes |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models - Implement translation and summarization |
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
| Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Analyze images and detect objects/features - Process and index video content - Integrate vision capabilities into applications - Build and deploy custom vision models |
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
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NEW QUESTION # 327
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to [email@domain.com] You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity type 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.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity
NEW QUESTION # 328
You have an Azure subscription.
You are building a chatbot that will use an Azure OpenAI model.
You need to deploy the model.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
You are building a chatbot that will use an Azure OpenAI model.
You need to deploy the model in your Azure subscription.
Key Steps to Deploy an Azure OpenAI Model for a Chatbot:
Apply for access to Azure OpenAI
Azure OpenAI is a gated service and requires approval before you can provision resources.
Provision an Azure OpenAI resource
After approval, you must provision an Azure OpenAI resource in your subscription.
Deploy the GPT model
Since the chatbot requires natural language understanding and conversation, you deploy a GPT model (e.g., GPT-4, GPT-3.5).
Actions That Are Not Required for this Question:
Provision Azure API Management # Not needed for basic deployment (used only for advanced API management scenarios).
Deploy the embeddings model # Used for search/RAG, not necessary for chatbot baseline.
Deploy the DALL-E model # Used for image generation, not chatbot text responses.
Correct Order:
Apply for access to Azure OpenAI.
Provision an Azure OpenAI resource.
Deploy the GPT model.
Verified Answer (Sequence):
Apply for access to Azure OpenAI
Provision an Azure OpenAI resource
Deploy the GPT model
Microsoft References:
Quickstart: Get started with Azure OpenAI
Deploying models in Azure OpenAI
NEW QUESTION # 329
You are developing a text processing solution.
You have the function shown below.
Answer:
Explanation:
Explanation
NEW QUESTION # 330
You build a chatbot that uses the Azure OpenAI GPT 3.5 model.
You need to improve the quality of the responses from the chatbot. The solution must minimize development effort.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Answer: D,E
Explanation:
To quickly improve a GPT-3.5 chatbot's response quality with minimal development effort:
Provide grounding content (B): Ground the model with authoritative data (e.g., the travel agent's policies, inventory, fares) using Azure OpenAI on your data or a lightweight RAG pattern. Grounding reduces hallucinations and makes answers more accurate and context-aware without retraining the model. This typically involves configuring a data connection (Azure Cognitive Search/Blob) and augmenting prompts- far less effort than training.
Add sample request/response pairs (C): Use few-shot examples directly in the system/user messages to demonstrate ideal answers, style, and formatting. Few-shot prompting is a core prompt-engineering technique that meaningfully boosts quality with minimal code changes or infrastructure.
Options A, D, and E involve model training or retraining (fine-tuning or building a custom LLM), which require data preparation, experimentation, monitoring, and higher cost/complexity-contrary to the "minimize development effort" constraint.
Microsoft References
Prompt engineering with examples (few-shot) to improve output quality:
https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering Azure OpenAI "on your data" (grounding to improve relevance and accuracy):
https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data Guidance comparing prompt engineering/grounding with fine-tuning and when to use each:
https://learn.microsoft.com/azure/ai-services/openai/concepts/fine-tuning How-to: Fine-tuning (shows added complexity vs. prompt techniques):
https://learn.microsoft.com/azure/ai-services/openai/how-to/fine-tuning
NEW QUESTION # 331
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 332
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