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| 1 | +### YamlMime:ModuleUnit |
| 2 | +uid: learn.azure.build-enterprise-ai-agents-with-java-spring.10-knowledge-check |
| 3 | +title: Knowledge check |
| 4 | +metadata: |
| 5 | + title: Knowledge Check |
| 6 | + description: Test your knowledge of Spring AI and Azure deployment. |
| 7 | + ms.date: 03/16/2025 |
| 8 | + author: KarlErickson |
| 9 | + ms.author: karler |
| 10 | + ms.reviewer: jbalderas, gok |
| 11 | + ms.topic: unit |
| 12 | + ms.custom: devx-track-java |
| 13 | + ms.collection: ce-skilling-ai-copilot |
| 14 | +durationInMinutes: 5 |
| 15 | +quiz: |
| 16 | + questions: |
| 17 | + - content: Which Spring AI component is used for generating vector embeddings? |
| 18 | + choices: |
| 19 | + - content: ChatClient |
| 20 | + isCorrect: false |
| 21 | + explanation: ChatClient isn't correct. The ChatClient offers a fluent API for communicating with AI models. |
| 22 | + - content: EmbeddingModel |
| 23 | + isCorrect: true |
| 24 | + explanation: Correct. EmbeddingModel is an interface for integration with embedding models in AI and can be used for generating vector embeddings. |
| 25 | + - content: QuestionAnswerAdvisor |
| 26 | + isCorrect: false |
| 27 | + explanation: While QuestionAnswerAdvisor uses vector embeddings for vector similarity search, it can't be used directly for generating embeddings. |
| 28 | + - content: AIClient |
| 29 | + isCorrect: false |
| 30 | + explanation: AIClient isn't a valid Spring AI component. |
| 31 | + - content: What PostgreSQL extension is required for vector similarity search? |
| 32 | + choices: |
| 33 | + - content: pg_similarity |
| 34 | + isCorrect: false |
| 35 | + explanation: The pg_similarity extension isn't used for vector similarity search. |
| 36 | + - content: pg_trgm |
| 37 | + isCorrect: false |
| 38 | + explanation: The pg_trgm extension isn't used for vector similarity search. |
| 39 | + - content: pgvector |
| 40 | + isCorrect: true |
| 41 | + explanation: Correct. The pgvector extension adds vector similarity search capabilities to PostgreSQL, which is essential for implementing RAG patterns with vector embeddings. |
| 42 | + - content: pg_vector_ops |
| 43 | + isCorrect: false |
| 44 | + explanation: The pg_vector_ops extension isn't used for vector similarity search. |
| 45 | + - content: Which Azure Container Apps feature enables automatic scaling based on HTTP traffic? |
| 46 | + choices: |
| 47 | + - content: HTTP Scale Rules |
| 48 | + isCorrect: true |
| 49 | + explanation: Correct. HTTP Scale Rules in Azure Container Apps enable automatic scaling based on incoming HTTP request concurrency. |
| 50 | + - content: Traffic Manager |
| 51 | + isCorrect: false |
| 52 | + explanation: Traffic Manager isn't used for automatic scaling based on HTTP traffic. |
| 53 | + - content: Load Balancer |
| 54 | + isCorrect: false |
| 55 | + explanation: Load Balancer isn't used for automatic scaling based on HTTP traffic. |
| 56 | + - content: Application Gateway |
| 57 | + isCorrect: false |
| 58 | + explanation: Application Gateway isn't used for automatic scaling based on HTTP traffic. |
| 59 | + - content: What is the purpose of the ChatClient in Spring AI? |
| 60 | + choices: |
| 61 | + - content: Managing database connections |
| 62 | + isCorrect: false |
| 63 | + explanation: The ChatClient isn't used for managing database connections. |
| 64 | + - content: Processing HTTP requests |
| 65 | + isCorrect: false |
| 66 | + explanation: The ChatClient isn't used for processing HTTP requests. |
| 67 | + - content: Interacting with language models |
| 68 | + isCorrect: true |
| 69 | + explanation: Correct. The ChatClient is Spring AI's core abstraction for interacting with large language models (LLMs) like Azure OpenAI. |
| 70 | + - content: Handling authentication |
| 71 | + isCorrect: false |
| 72 | + explanation: The ChatClient isn't used for handling authentication. |
| 73 | + - content: Which configuration is required for Azure OpenAI integration in application.properties? |
| 74 | + choices: |
| 75 | + - content: Only the API key |
| 76 | + isCorrect: false |
| 77 | + explanation: The API key isn't sufficient for Azure OpenAI integration. |
| 78 | + - content: Only the endpoint URL |
| 79 | + isCorrect: false |
| 80 | + explanation: The endpoint URL isn't sufficient for Azure OpenAI integration. |
| 81 | + - content: Only the deployment name |
| 82 | + isCorrect: false |
| 83 | + explanation: The deployment name isn't sufficient for Azure OpenAI integration. |
| 84 | + - content: The API key, endpoint, and deployment name |
| 85 | + isCorrect: true |
| 86 | + explanation: Correct. Spring AI requires the API key, endpoint URL, and deployment name to properly configure Azure OpenAI integration. |
| 87 | + - content: Which Agentic workflow uses multiple AI roles to improve content quality? |
| 88 | + choices: |
| 89 | + - content: The Chain workflow |
| 90 | + isCorrect: false |
| 91 | + explanation: The Chain workflow breaks complex tasks into a series of steps |
| 92 | + - content: The Evaluator-Optimizer workflow |
| 93 | + isCorrect: true |
| 94 | + explanation: The Evaluator-Optimizer workflow uses writer and editor AI roles in a feedback loop to improve content quality. |
| 95 | + - content: The Routing workflow |
| 96 | + isCorrect: false |
| 97 | + explanation: The Routing workflow directs input to specialized handlers |
| 98 | + - content: The Parallelization workflow |
| 99 | + isCorrect: false |
| 100 | + explanation: The Parallelization workflow is ideal for processing large volumes of independent items. |
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