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Spring AI Introduction

Total questions: 10

Worksheet time: 5mins

Name
Class
Date
1.

What is the main purpose of Spring AI?

a)

To provide abstractions that allow for the use of various AI models and services with minimal code changes

b)

Offer higher-level functionalities, such as Q&A or chatting with documentation

c)

All of the above

d)

To simplify the development of applications that use artificial intelligence (AI) features

2.

How many main modules does Spring AI currently have?

a)

13

b)

10

c)

11

d)

12

3.

Which AI Service is NOT supported by Spring AI?

a)

Azure Open AI

b)

OpenAI

c)

Google Cloud AI

d)

None of the above

4.

Placeholder in PromptTemplate enclosed in:

a)

< >

b)

{ }

c)

[ ]

d)

( )

5.

To create input and receive output from AI model, Spring AI provides:

a)

Prompt & Ai Response classes

b)

Prompt & AI Result classes

c)

PromptMessage & Ai Response classes

d)

None of the above

6.

In the context of Spring AI, which of the following statements is correct?

a)

AiResponse and Generation classes are used for network communication.

b)

AiResponse and Generation classes are used to represent the response of an AI model.

c)

AiResponse and Generation classes are used for data storage.

d)

AiResponse and Generation classes are used for creating inputs to the AI model.

7.

Which OuputParser Implementation is used for parsing AiModel Response to a defined Java Object?

a)

ObjectOutputParser

b)

BeanOutputParser

c)

ListOutputParser

d)

MapOutputParser

8.

What does ETL stand for?

a)

Encode, Transmit, Log

b)

Export, Transfer, Link

c)

Enter, Translate, Load

d)

Extract, Transform, Load

9.

What is the role of an ETL pipeline in the context of Retrieval-Augmented Generation (RAG)?

a)

ETL pipeline is used to deploy the RAG technique.

b)

ETL pipeline is used to evaluate the performance of the RAG technique.

c)

ETL pipeline serves as a data preparation step within the RAG.

d)

ETL pipeline is used to improve the RAG technique.

10.

What is the purpose of using Retrieval-Augmented Generation (RAG)?

a)

To improve the accuracy of generation.

b)

To manage model token limits and document retrieval, saving resources and enhancing prompt effectiveness.

c)

To speed up generation time.

d)

To enhance data storage capabilities.