AIP-C01 Practice Test 15 — 15 Questions

Question 1

A developer needs to ensure that an application only uses approved foundation models. How can they govern this centrally?

Accepted Answer

Use Service Control Policies (SCPs) to deny non-approved models. Using AWS Organizations SCPs, an administrator can restrict the bedrock:InvokeModel action to a list of allowed model IDs.

Answer

Create an IAM role for each model

Answer

Use Amazon Cognito

Answer

Hardcode the model ID in the application code

Question 2

An organization is concerned about 'Model Hallucinations' and needs a way to ground model outputs in trusted company documents. Which service supports this?

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases provide RAG (Retrieval-Augmented Generation), which grounds model responses in provided documents.

Answer

Amazon SageMaker Feature Store

Answer

AWS Glue Data Catalog

Answer

Amazon DynamoDB

Question 3

When fine-tuning a model on Amazon Bedrock, which THREE security considerations are critical?

Accepted Answer

Limiting IAM permissions for the fine-tuning job. Data governance, encryption, and access control are critical for fine-tuning security.

Answer

Using a root user account to launch the training job

Answer

Making the training dataset publicly readable

Question 4

An enterprise requires that all Generative AI model outputs are logged for compliance, but logs must not contain any PII. How can this be achieved in an AWS environment using Amazon Bedrock?

Accepted Answer

Enable Bedrock Model Invocation Logging with Guardrails PII redaction enabled. Configuring Model Invocation Logging in Bedrock to send logs to CloudWatch or S3, combined with Guardrails PII redaction, ensures compliant logging.

Answer

Write a Lambda function to strip logs before writing to S3

Answer

Use IAM policies to prevent logs from being written

Answer

Use AWS CloudTrail to log API calls and enable redaction in CloudWatch

Question 5

A team is storing sensitive documents for RAG. They need to ensure that the document chunks in the vector database are encrypted at rest. How?

Accepted Answer

Configure the vector database (e.g., OpenSearch) with KMS encryption. When creating a Knowledge Base in Bedrock, you must specify a KMS key to encrypt the underlying vector store (e.g., OpenSearch Serverless).

Answer

Use TLS for all connections

Answer

Use Amazon S3-managed encryption

Answer

Encrypt the source S3 bucket only

Question 6

A researcher wants to ensure the model output does not reference external websites. How can this be enforced?

Accepted Answer

Configure a Guardrail with a Denied Topics policy. Bedrock Guardrails can be configured with a Word Policy to deny specific patterns or regex that identify URLs, or a topic policy to block external references.

Answer

Use a custom system prompt to tell the model not to use URLs

Answer

Disable model streaming

Answer

Use IAM to block internet access for the application

Question 7

A company is using RAG with Amazon Bedrock Knowledge Bases. They need to ensure that users can only retrieve documents they are authorized to see. How should this be implemented?

Accepted Answer

Use the filters attribute in the Retrieve API call based on document metadata. You must implement metadata filtering in the Knowledge Base query. When invoking Retrieve, you can apply filters based on user attributes stored in the document metadata.

Answer

Encrypt the documents with different KMS keys

Answer

Create separate Knowledge Bases for every user

Answer

Apply IAM policies to the S3 bucket

Question 8

Your organization wants to evaluate the quality and safety of model outputs using a formal testing framework. Which service is designed for this?

Accepted Answer

Amazon Bedrock Model Evaluation. Amazon Bedrock Model Evaluation allows you to run systematic evaluations of models based on custom or built-in datasets to measure quality and safety.

Answer

AWS CloudWatch Synthetics

Answer

Amazon SageMaker Pipelines

Answer

AWS CodePipeline

Question 9

A company is using Amazon Bedrock to build a customer support chatbot and needs to ensure that PII is masked before the request reaches the foundation model. Which feature should the developer implement?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails provides built-in PII redaction capabilities to identify and mask sensitive information in both prompts and responses.

Answer

AWS KMS with envelope encryption

Answer

Amazon Macie

Answer

AWS WAF with custom regex rules

Question 10

Which THREE of the following are components of a robust AI governance framework in an AWS environment?

Accepted Answer

Implement Amazon Bedrock Guardrails. Governance involves auditing (CloudTrail), safety configuration (Guardrails), and access control (IAM).

Answer

Provide all users with AdministratorAccess

Answer

Publicly host the model endpoint

Question 11

A developer is using Amazon Bedrock to build a customer support chatbot. The model is hallucinating frequently when retrieving internal company knowledge. Which evaluation methodology should the developer implement to quantify the ground truth accuracy of the RAG pipeline?

Accepted Answer

Implement a RAGAS-based evaluation pipeline to measure faithfulness and answer relevance.. Retrieval-Augmented Generation (RAG) evaluation requires metrics like RAGAS or TruLens to measure faithfulness, answer relevance, and context precision against a golden dataset.

Answer

Increase the temperature setting to 1.0 to encourage more creative responses.

Answer

Use Amazon CloudWatch logs to calculate the latency of every inference call.

Answer

Run a standard BLEU score evaluation against the model's training data.

Question 12

A developer is designing a RAG system and wants to ensure high-quality retrieval. Which THREE techniques should be implemented to improve semantic search relevance?

Accepted Answer

Using a reranking model to score retrieved documents. Hybrid search, reranking, and semantic chunking are standard best practices for enhancing RAG retrieval accuracy.

Answer

Increasing the vector dimension size to maximum

Answer

Using only fixed-size overlap chunking

Question 13

A healthcare organization must ensure that PII is removed from documents before they are stored in an Amazon Bedrock Knowledge Base. Which THREE actions should the developer take?

Accepted Answer

Configure the Knowledge Base to use a private VPC endpoint to prevent data exfiltration.. Data preparation involves identifying, masking, and auditing data before it reaches the vector store.

Answer

Manually encrypt all files using AWS KMS before uploading to S3.

Answer

Enable Amazon Bedrock Data Protection policies to automatically strip PII during vectorization.

Question 14

How can a developer ensure that the retrieval step in a RAG application focuses only on the most relevant documents when the knowledge base contains millions of files?

Accepted Answer

Implement metadata filtering in the query.. Using metadata filtering allows the system to narrow down the search space before performing vector similarity searches, increasing accuracy.

Answer

Increase the vector dimension size.

Answer

Use a larger model for embedding.

Answer

Reduce the chunk size.

Question 15

When designing an agentic solution, which THREE aspects of security must be considered?

Accepted Answer

IAM least privilege. Security requires IAM least privilege, data encryption, and input safety (Guardrails).

Answer

Making all API endpoints public

Answer

Sharing API keys in the prompt