AIP-C01 Implementation And Integration Practice Test 2 — 15 Questions

Question 1

A developer is building a retrieval-augmented generation (RAG) application using Amazon Bedrock Knowledge Bases. The knowledge base is backed by an Amazon OpenSearch Serverless vector store. Users report that responses sometimes include outdated information even though the source documents have been updated in Amazon S3. Which TWO actions should the developer take to ensure the knowledge base reflects the latest document versions? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Configure the knowledge base to use incremental sync and schedule it to run automatically after S3 updates.. To keep a Bedrock knowledge base current, you need to run ingestion jobs that detect and apply changes. Incremental sync updates only modified documents, reducing cost and time. Automating the sync with EventBridge and Lambda when S3 objects change ensures timely updates without manual intervention. Together, these actions maintain accurate and up-to-date responses.

Answer

Use Amazon Bedrock's automatic synchronization feature, which continuously monitors S3 and updates the knowledge base in real time.

Answer

Manually trigger a full sync of the knowledge base after each document update.

Answer

Enable versioning on the S3 bucket and configure the knowledge base to use the latest object version.

Question 2

When using the Amazon Bedrock Converse API, which parameter enables the model to perform tool use by providing a definition of available functions?

Accepted Answer

toolConfig. The tools parameter in the Converse API allows you to define a list of tools for the model to use.

Answer

inferenceConfig

Answer

system

Answer

additionalModelRequestFields

Question 3

You are deploying a model from SageMaker JumpStart. You need to ensure that the model inference container has access to private VPC resources. What must you configure?

Accepted Answer

Configure Subnets and Security Groups in the Model creation. You must configure the VPC settings (Subnets and Security Groups) in the SageMaker Model configuration to allow network access.

Answer

Set the IAM role to 'AdministratorAccess'

Answer

Update the JumpStart model Dockerfile

Answer

Enable AWS PrivateLink on the S3 bucket

Question 4

You are designing an Agentic AI solution using Bedrock Agents. Which THREE components must you configure to enable the agent to interact with an external API?

Accepted Answer

Action Group. Option B (Action Group) is correct because an action group is the Bedrock Agents construct that defines the set of tasks the agent can invoke against external APIs, binding the agent's reasoning to concrete operations. Option C (OpenAPI Schema) is correct because the action group requires an OpenAPI schema (API schema) that describes the external API's endpoints, parameters, and request/response structures so the agent knows how to call it. Option D (Lambda Function) is correct because the action group must be backed by an executor — typically an AWS Lambda function — that receives the agent's invocation and performs the actual API call to the external service. Option A (Knowledge Base) is not required here because knowledge bases are for retrieval-augmented generation over documents/data, not for invoking external APIs. Option E (Guardrail) is not required because guardrails enforce content safety and policy filtering, which is unrelated to enabling API interaction.

Answer

Knowledge Base

Answer

Guardrail

Question 5

When using Bedrock Knowledge Bases, which vector database is integrated natively to support managed RAG workflows?

Accepted Answer

Amazon OpenSearch Serverless. Amazon Bedrock Knowledge Bases natively integrates with Amazon OpenSearch Serverless as its managed vector store, allowing RAG workflows to be set up without provisioning or managing vector database infrastructure. OpenSearch Serverless provides the vector search collection type that Bedrock uses to store and retrieve embeddings during retrieval-augmented generation. This native integration means customers can point Bedrock at an OpenSearch Serverless vector index and have chunking, embedding, and retrieval handled automatically.

Answer

Amazon DynamoDB

Answer

Amazon ElastiCache

Answer

Amazon RDS for PostgreSQL

Question 6

You are integrating a SageMaker JumpStart model via the InvokeEndpoint API. The model is failing to process images due to a schema mismatch. Which format does the JumpStart image input expect by default for standard vision models?

Accepted Answer

Base64 encoded string in the JSON input field. SageMaker JumpStart vision models (e.g., image classification, object detection) expect the input image to be Base64-encoded and placed in the JSON payload under the appropriate input field (commonly 'image' or 'data'). The InvokeEndpoint API sends JSON, so raw binary cannot be transmitted directly. The model's inference container decodes the Base64 string back to bytes before preprocessing.

Answer

A URL pointing to an S3 object

Answer

Multi-part form data

Answer

Raw binary stream in the request body

Question 7

An application uses Amazon Bedrock InvokeModel to generate text. You notice that the output length is consistently truncated. Which parameter should you adjust to ensure the full response is captured?

Accepted Answer

Increase the max_tokens parameter. In Amazon Bedrock InvokeModel, the max_tokens parameter (or its model-specific equivalent like max_gen_len) controls the maximum number of tokens the model can generate in the response. If output is consistently truncated, increasing max_tokens allows the model to produce a longer completion. The other parameters affect randomness or stopping criteria, not the length cap.

Answer

Increase the temperature parameter

Answer

Increase the top_p parameter

Answer

Decrease the stop_sequences parameter

Question 8

You are implementing a Bedrock Guardrail. You need to ensure the model filters out specific PII (Personally Identifiable Information). Which capability should be configured?

Accepted Answer

Sensitive Information Filter. The Sensitive Information Filter in Bedrock Guardrails is specifically designed to detect and block or mask PII such as names, addresses, phone numbers, and other sensitive data. It uses pattern matching and machine learning to identify entities like SSNs, credit card numbers, and custom regex patterns. This directly addresses the requirement to filter out specific PII, unlike other guardrail components that focus on harmful content or topic restrictions.

Answer

Content Filter

Answer

Denied Topics

Answer

Word Policy

Question 9

Which TWO metrics are natively available in Amazon CloudWatch for monitoring Bedrock model invocation performance?

Accepted Answer

InvocationLatency. Option B (InvocationLatency) is correct because Amazon Bedrock natively publishes the InvocationLatency metric to CloudWatch, which measures the time taken for a model invocation request to complete, directly reflecting model invocation performance. Option D (InvocationClientErrors) is correct because Bedrock also natively emits the InvocationClientErrors metric to CloudWatch, capturing the count of client-side errors (such as throttling or validation failures) during model invocations, which is essential for monitoring invocation health. Option A (VectorEmbeddingAccuracy) is not a CloudWatch metric provided by Bedrock; accuracy evaluation is handled through Bedrock model evaluation jobs, not runtime CloudWatch metrics. Option C (GPUUtilization) is not natively exposed by Bedrock in CloudWatch, since Bedrock is a fully managed service and does not surface underlying GPU infrastructure metrics to customers. Option E (ModelTrainingLoss) is not applicable because Bedrock does not expose training loss metrics in CloudWatch; Bedrock focuses on inference of foundation models rather than customer-visible training telemetry.

Answer

VectorEmbeddingAccuracy

Answer

GPUUtilization

Answer

ModelTrainingLoss

Question 10

When using the Bedrock Converse API, which object allows you to maintain context across multiple turns of a conversation?

Accepted Answer

messages. The Bedrock Converse API uses the `messages` array to hold the full conversation history, including alternating user and assistant turns. Each turn is represented as a message with a role and content, and by passing the entire array on subsequent calls, the model can see prior context. This is the standard mechanism for multi-turn conversations, as it explicitly maintains state across requests.

Answer

inferenceConfig

Answer

additionalModelRequestFields

Answer

system

Question 11

In the context of Bedrock Agents, what is the purpose of the 'Instruction' field in the agent configuration?

Accepted Answer

It serves as the system prompt to define the agent's behavior. The instruction field serves as the system prompt that defines the agent's persona and objective.

Answer

It defines the Knowledge Base search parameters

Answer

It stores user credentials for the API

Answer

It contains the list of API definitions

Question 12

You are designing a RAG pipeline. Which step in the ingestion process is responsible for converting unstructured text into a numerical representation?

Accepted Answer

Embedding. Embedding is the step that converts text chunks into dense numerical vectors (embeddings) that capture semantic meaning. These vectors are then stored in a vector database so that similarity search can retrieve relevant chunks at query time. Without embedding, the RAG pipeline cannot perform semantic retrieval.

Answer

Chunking

Answer

Indexing

Answer

Parsing

Question 13

Which AWS service is best used to store and version control your prompt templates for Bedrock?

Accepted Answer

Amazon Bedrock Prompt Management. Amazon Bedrock Prompt Management is the correct answer because it is a purpose-built feature that lets you create, store, version, and reuse prompt templates natively within Bedrock. It provides versioning, aliases, and variables so you can iterate on prompts and roll back to previous versions without external tooling. This integrates directly with Bedrock's InvokeModel and agent APIs, making it the most operationally efficient choice for prompt lifecycle management.

Answer

AWS Systems Manager Parameter Store

Answer

AWS CodeCommit

Answer

Amazon S3

Question 14

What is the primary role of an 'Embeddings Model' in a RAG-based Generative AI application?

Accepted Answer

To convert input text into high-dimensional numerical vectors. An embeddings model in a RAG application converts input text (queries and document chunks) into high-dimensional numerical vectors that capture semantic meaning. These vectors are stored in a vector database and used for similarity search to retrieve relevant context for the LLM.

Answer

To generate the final natural language answer

Answer

To store document chunks for long-term retrieval

Answer

To parse PDF files into plain text

Question 15

Which prompt engineering technique involves providing the model with a few examples of desired input-output pairs to guide the generation?

Accepted Answer

Few-shot prompting. Few-shot prompting is the technique of including a small number of example input-output pairs in the prompt to demonstrate the desired task and format, guiding the model to generalize to the new input. This contrasts with zero-shot, where no examples are given. The examples act as in-context learning signals.

Answer

Chain-of-thought

Answer

System priming

Answer

Zero-shot prompting