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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - Analyze images and videos using Azure AI Vision - Implement custom vision models - Extract information using OCR and form recognition |
| Topic 2: Implement Natural Language Processing Solutions | - Analyze text using Azure AI Language - Implement question answering and conversational AI - Translate and interpret language using Azure AI services |
| Topic 3: Implement Knowledge Mining and Document Intelligence | - Enrich and index data for search scenarios - Build Azure AI Search solutions - Implement document intelligence solutions |
| Topic 4: Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions |
| Topic 5: Plan and Manage an Azure AI Solution | - Manage security, compliance, and responsible AI - Select appropriate Azure AI services - Plan solution requirements and architecture |
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NEW QUESTION # 223
You have a bot that was built by using the Microsoft Bot Framework composer as shown in the following exhibit.
Use the drop-down menus 10 select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 224
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: B,C
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 # 225
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:
Azure Databricks is an Apache Spark-based analytics platform. # Yes
* Azure Analysis Services is used for transactional workloads. # No
* Azure Data Factory orchestrates data integration workflows. # Yes
Let's break down each statement:
* Azure Databricks is an Apache Spark-based analytics platform.
* True.
* Azure Databricks is a fast, easy, and collaborative Apache Spark-based big data analytics platform.
* It is designed for data engineering, machine learning, and AI workloads.
* Answer: Yes
* Azure Analysis Services is used for transactional workloads.
* False.
* Azure Analysis Services is an analytical service for semantic data modeling, OLAP (online analytical processing), and BI (business intelligence).
* Transactional workloads belong to OLTP (online transaction processing) databases such as Azure SQL Database.
* Answer: No
* Azure Data Factory orchestrates data integration workflows.
* True.
* Azure Data Factory (ADF) is a cloud-based ETL and data integration service that orchestrates and automates data movement and transformation across on-premises and cloud systems.
* Answer: Yes
NEW QUESTION # 226
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation
NEW QUESTION # 227
You are building a chatbot.
You need to ensure that the bot will recognize the names of your company's products and codenames.
The solution must minimize development effort.
Which Azure Cognitive Service for Language service should you include in the solution?
Answer: D
NEW QUESTION # 228
......
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