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Bedrock Immersion Day Quiz

Total questions: 10

Worksheet time: 6mins

Name
Class
Date
1.

What type of knowledge bases can be created with AWS Bedrock?

a)

Unstructured data knowledge bases

b)

Structured data knowledge bases

c)

Both unstructured and structured data knowledge bases

d)
  1. Neither unstructured nor structured data knowledge bases

2.

What data sources can be ingested into a Bedrock knowledge base?

a)

PDFs, Word documents

b)

Excel Files

c)

CSV files

d)

All of the above

3.

How are knowledge bases queried in Bedrock?

a)

Using SQL statements

b)

Using SPARQL queries

c)

Using natural language queries

d)

Using graph traversal APIs

4.

Which AWS services are not supported as vector databases by Knowledge Bases for Amazon Bedrock?

a)

Amazon OpenSearch Serverless

b)

Pinecone

c)

Redis Enterprise Cloud

d)

Amazon Neptune

5.

Select all the API's offered by Bedrock Agents to fetch results from Knowledge Bases ? (Multi Select)

a)

Retrieve API

b)

RetrieveAndGenerate API

c)

Receive API

d)

ReceiveAndGenerate API

6.

Which is not a type of learning in machine learning ?

a)

Supervised Learning

b)

Unsupervised Learning

c)

ReInforcement Learning

d)

Collective Learning

7.

What are the Common approaches for customising foundation models (FMs) ? (Muti-choice Question)

a)

Prompt

Engineering

b)

Retrieval

Augmented

Generation

c)

Fine-tuning

d)

Re-training

8.

RAG is an acronym of ?

a)

Retrieval Augmented Generation

b)

Retrive and Generate

c)

Receive and Generate

d)

Review and Generate

9.

What is an Vector Embedding ?

a)

Vector embeddings are a way to convert words and sentences into numbers

b)

Vector embedding are a way to convert words and sentences into natural language

c)

Vector embedding are a way to convert words and sentences into simple language

d)

Vector embedding are a way to convert words and sentences into structural language

10.

Which model can be used to generate embeddings ? (Multi-Select)

a)

Titan Embeddings G1 - Text

b)

Titan Multimodal Embeddings G1

c)

Cohere Embed English

d)

Anthropic Embed