AIP-C01 Practice Test 9 — 10 Questions

Question 1

To prevent accidental deletion of a Knowledge Base, which feature should be applied?

Accepted Answer

Apply an IAM policy denying DeleteKnowledgeBase. Apply an IAM policy denying DeleteKnowledgeBase. AWS Identity and Access Management (IAM) policies are the primary mechanism for controlling access to AWS actions, including the DeleteKnowledgeBase action for Amazon Bedrock Knowledge Bases. By explicitly denying this action, you prevent any principal (user, role, or service) from deleting the knowledge base, even if they have other permissions that would otherwise allow it. This is a direct, preventive control that aligns with the principle of least privilege.

Answer

Use AWS Backup

Answer

Enable MFA Delete on S3

Answer

Enable S3 Versioning

Question 2

You have a fleet of SageMaker endpoints. You want to implement an automatic cost reduction strategy during off-peak hours for specific endpoints. Which approach is most efficient?

Accepted Answer

Configuring Application Auto Scaling with scheduled actions. Application Auto Scaling for SageMaker endpoints supports scheduled scaling actions that adjust the desired instance count based on a cron or rate expression. This allows you to automatically scale in (reduce instances) during off-peak hours and scale out during peak hours, directly reducing cost without manual intervention. It is the most efficient because it is automated, precise, and integrates natively with SageMaker endpoints.

Answer

Using spot instances for all endpoints

Answer

Deactivating the endpoint manually

Answer

Updating the endpoint configuration

Question 3

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

Accepted Answer

IAM least privilege. Option B (IAM least privilege) is correct because an agentic solution typically assumes roles or service accounts to call tools, models, and data sources, so granting only the minimal permissions needed limits blast radius if the agent is manipulated or compromised. Option D (Applying Guardrails for input/output) is correct because guardrails filter and validate prompts, tool invocations, and model responses, mitigating prompt injection, data leakage, and unsafe or off-policy actions. Option E (Encryption of data at rest and in transit) is correct because agent workflows move sensitive context, credentials, and results across networks and stores, so TLS for transit and KMS-backed encryption for storage protect confidentiality and integrity. Option A (Making all API endpoints public) is not appropriate because exposing every endpoint without authentication or authorization dramatically expands the attack surface. Option C (Sharing API keys in the prompt) is not appropriate because embedding secrets in prompts risks leakage through logs, model context, or outputs and violates credential-handling best practice.

Answer

Making all API endpoints public

Answer

Sharing API keys in the prompt

Question 4

A compliance team requires that all prompts containing sensitive data be encrypted. How does Amazon Bedrock handle this during the request?

Accepted Answer

Amazon Bedrock uses TLS 1.2+ for encryption in transit.. Amazon Bedrock encrypts all data in transit using TLS 1.2 or higher, including prompts sent to the API. This is automatic and does not require manual encryption or special encoding by the developer. Therefore, prompts containing sensitive data are encrypted in transit by default.

Answer

Bedrock only supports encrypted prompts if a VPC endpoint is used.

Answer

The prompt must be base64 encoded.

Answer

The developer must manually encrypt the prompt string before calling the API.

Question 5

A firm wants to audit the specific version of a foundation model used for every inference request. How can they achieve this?

Accepted Answer

Enable Bedrock Model Invocation Logging. Bedrock Model Invocation Logging captures each invocation's request and response metadata — including the model ID and, when applicable, the specific model version or inference profile — and delivers it to CloudWatch Logs or S3. This provides the audit trail needed to prove which foundation model version handled each request.

Answer

Query Amazon SageMaker Model Monitor

Answer

Enable CloudTrail logs

Answer

Use AWS Config to track model changes

Question 6

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. Encryption at rest for a vector database is enforced at the storage layer of that database, so configuring the vector store (e.g., Amazon OpenSearch Serverless or Aurora pgvector) with a KMS customer-managed key ensures the indexed embeddings and metadata are encrypted on disk. This is independent of how the source documents are encrypted in S3. KMS CMKs give the team control over key rotation and access policies.

Answer

Use TLS for all connections

Answer

Use Amazon S3-managed encryption

Answer

Encrypt the source S3 bucket only

Question 7

When should a developer choose to fine-tune a model versus using RAG?

Accepted Answer

When you need the model to learn a specific, consistent corporate tone.. RAG is best for grounding in changing data, while fine-tuning is better for changing the model's style, tone, or highly specialized domain language.

Answer

When you have very little data.

Answer

When you need to ensure the model does not hallucinate.

Answer

When the data changes daily.

Question 8

A company is using a third-party model via Bedrock and needs to ensure that the model provider does not use their data to train their base models. What must they do?

Accepted Answer

Understand that AWS does not use customer data to train their foundation models. AWS does not use customer data to train its foundation models. When using third-party models via Amazon Bedrock, the model provider also does not have access to customer data for training their base models. This is a fundamental data privacy commitment from AWS. Therefore, no additional action is required; the correct understanding is that AWS and its model providers do not use customer data for training. Enabling an opt-out setting is not necessary because the default is already no training on customer data.

Answer

Disable all logging

Answer

Enable the 'Opt-out' setting in the Bedrock console

Answer

Encrypt all data with a customer-managed key

Question 9

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 10

You are troubleshooting a SageMaker inference endpoint and need to see the container logs. Which service stores these logs?

Accepted Answer

Amazon CloudWatch Logs. SageMaker inference endpoints run containers on managed EC2 instances, and all stdout/stderr output from those containers is streamed to Amazon CloudWatch Logs under log groups named /aws/sagemaker/Endpoints/<endpoint-name>. This is where you inspect model loading errors, inference failures, and framework-level messages. CloudWatch is the native logging destination for nearly all AWS managed compute services, making it the correct answer here.

Answer

Amazon S3

Answer

Amazon DynamoDB

Answer

AWS CloudTrail