AIF-C01 Practice Test 7 — 10 Questions

Question 1

A developer wants to store and search vector embeddings for a RAG application. Which AWS-managed vector store option is serverless and can be used with Amazon Bedrock?

Accepted Answer

Amazon OpenSearch Serverless. Amazon OpenSearch Serverless is a serverless option for vector search. The others are either not serverless or not fully managed by AWS.

Answer

Amazon RDS for MySQL

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 2

A company is building a generative AI application to answer questions from a large set of technical manuals. Which TWO services or features can be used together in a RAG architecture on AWS? (Choose TWO.)

Accepted Answer

Amazon OpenSearch Serverless. A RAG architecture requires a knowledge base for indexing documents and a vector store for storing embeddings. Bedrock Knowledge Bases can use OpenSearch Serverless as a vector store. SageMaker is for training, not retrieval. Guardrails are safety layers. Lambda is for action groups, not core RAG components.

Answer

Amazon SageMaker

Answer

AWS Lambda

Answer

Amazon Bedrock Guardrails

Question 3

A developer implements a RAG pipeline with Amazon Bedrock and Amazon OpenSearch Serverless. Users report that the chatbot sometimes returns off-topic responses. Investigation shows the retrieved chunks are semantically unrelated to the queries. What is the MOST likely cause?

Accepted Answer

The chunking strategy is not optimised for the document type, or the embedding model is not well-suited. Poor embedding quality or chunking strategy can cause irrelevant retrieval. The other options relate to generation or permission issues.

Answer

The LLM used for generation has too few parameters

Answer

The agent's action group has misconfigured Lambda functions

Answer

The vector store index is not encrypted at rest

Question 4

A company wants to build a generative AI application that can automatically classify customer feedback into positive, neutral, or negative sentiments. Which foundation model capability is BEST suited for this task?

Accepted Answer

Text classification. Text classification is a common use case for foundation models, which can be prompted to output a label. Sentiment analysis is a specific type of text classification.

Answer

Translation

Answer

Text generation

Answer

Text summarization

Question 5

A developer is using Bedrock Studio to prototype a summarization application. They want to quickly test different foundation models and prompts without writing code. What should they use?

Accepted Answer

Bedrock Playground. Bedrock Playground is a no-code interface within Bedrock Studio for experimenting with models and prompts. Knowledge Bases, Agents, and Guardrails are not designed for rapid prototyping of prompts and models.

Answer

Bedrock Knowledge Bases

Answer

Bedrock Agents

Answer

Bedrock Guardrails

Question 6

A data scientist wants to quickly build a supervised learning model for binary classification on a tabular dataset with 10,000 rows and 200 features. The dataset has some missing values and requires minimal code. Which AWS service should the data scientist use?

Accepted Answer

Amazon SageMaker Autopilot. Amazon SageMaker Autopilot is the correct choice because it automatically performs data preprocessing (including handling missing values), feature engineering, model selection, and hyperparameter tuning for supervised learning tasks like binary classification. It requires minimal code—users can simply point to a tabular dataset in Amazon S3 and specify the target column, and Autopilot will automatically train and evaluate multiple candidate models, making it ideal for quickly building a binary classifier on a 10,000-row, 200-feature dataset with missing values.

Answer

Amazon SageMaker Studio Lab

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker JumpStart

Question 7

A team has created a knowledge base in Amazon Bedrock for a Q&A application. After updating the source documents, they notice that the model still returns old information. What is the MOST likely cause?

Accepted Answer

The knowledge base has not been resynchronized after updating the documents. The vector store must be resynchronized after document updates. Chunking changes, model selection, and guardrails would not cause stale data if the vector store is not refreshed.

Answer

The chunking strategy is incorrect

Answer

The foundation model has a limited context window

Answer

The guardrails are filtering out the new information

Question 8

Which of the following is a primary benefit of using Bedrock Agents for building generative AI applications?

Accepted Answer

Orchestrate multi-step tasks and call external APIs via action groups. Bedrock Agents enable multi-step reasoning and tool use, allowing the model to perform complex sequences of actions. Fine-tuning is not an agent capability. Guardrails are separate. Prompt optimization is done at the prompt level, not by agents.

Answer

Automatically fine-tune the underlying model on new data

Answer

Optimize prompts automatically without any manual tuning

Answer

Guarantee that the model's responses are factually correct

Question 9

A company uses Amazon Bedrock Agents to automate a multi-step customer support workflow. The agent needs to query a customer database and update a ticket system. Which TWO components are required to enable the agent to interact with these external systems?

Accepted Answer

Lambda functions that implement the business logic for each action. Action groups define the functions the agent can call, and Lambda functions implement the business logic to interact with databases and APIs. Knowledge Bases provide static information, no tool use. Guardrails filter content, not execute actions. Vector stores store embeddings.

Answer

A vector store like Amazon OpenSearch Serverless

Answer

Bedrock Guardrails

Answer

Bedrock Knowledge Bases

Question 10

A company uses Amazon Bedrock to generate marketing copy. They want to evaluate the quality of generated text against human-written reference texts using automated metrics. Which metric measures the overlap of n-grams between generated and reference text?

Accepted Answer

ROUGE. ROUGE measures n-gram overlap and is commonly used for summarisation. BLEU is for translation, BERTScore uses embeddings, and Perplexity measures language model confidence.

Answer

Perplexity

Answer

BLEU

Answer

BERTScore