AIP-C01 Practice Test 2 — 10 Questions

Question 1

A developer needs to integrate a foundation model that requires custom fine-tuning for a specific domain language. Which service provides the environment for this task?

Accepted Answer

Amazon Bedrock Custom Models. Amazon Bedrock Custom Models allow for fine-tuning base models on private datasets to improve performance on specific tasks or domains.

Answer

AWS Glue DataBrew

Answer

Amazon SageMaker JumpStart

Answer

Amazon Kendra

Question 2

A developer needs to measure the quality of a RAG application's responses. Which tool within Amazon Bedrock facilitates this?

Accepted Answer

Amazon Bedrock Model Evaluation.. Amazon Bedrock Model Evaluation allows users to evaluate their models or RAG pipelines against custom or built-in datasets.

Answer

Amazon CloudWatch Metrics.

Answer

AWS Glue Quality Rules.

Answer

Amazon SageMaker Model Monitor.

Question 3

Which TWO factors are critical when choosing a model for a RAG architecture that requires high grounding accuracy?

Accepted Answer

The model's support for citation and source attribution.. Accuracy in RAG relies on model reasoning capability and the quality of the retrieved chunks.

Answer

The model's training date.

Answer

The model's ability to generate images.

Answer

The number of parameters in the model.

Question 4

A firm needs to ensure that all Generative AI model training data, if used for fine-tuning, is compliant with GDPR. Where should the training data reside?

Accepted Answer

Region-compliant S3 buckets with restricted IAM access and encryption. The data should be stored in S3 buckets located within the region that complies with GDPR, with appropriate encryption and access controls.

Answer

Publicly accessible S3 buckets

Answer

Temporary local storage on the training instance

Answer

DynamoDB tables

Question 5

Which Amazon Bedrock feature allows developers to restrict user queries based on specific topics or harmful content categories?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails provide a structured way to define policies that filter input and output based on topics, PII, and harmful content.

Answer

Knowledge Base Vector Index policies

Answer

Cross-region inference

Answer

Model Invocation Logs

Question 6

A company is concerned about model provider policy compliance. Where can they find the specific data processing terms for their chosen model?

Accepted Answer

In the AWS Service Terms and individual model provider documentation.. The AWS Service Terms and the Bedrock documentation page specify the data handling and privacy commitments for each model provider.

Answer

In the Amazon Bedrock console 'Model Evaluation' tab.

Answer

By asking the model during an inference session.

Answer

In the Amazon CloudWatch monitoring dashboard.

Question 7

When designing a RAG system, why would a developer choose Amazon Titan Text Embeddings over a third-party embedding model?

Accepted Answer

It is optimized for Amazon Bedrock Knowledge Bases, ensuring seamless integration and scalability.. Amazon Titan Embeddings are optimized to work natively with Amazon Bedrock Knowledge Bases and provide high performance and integration benefits.

Answer

It supports multi-modal input processing natively.

Answer

It is the only model that supports English.

Answer

It prevents the model from hallucinating.

Question 8

Which metric should a developer monitor to ensure the RAG system remains performant as usage increases?

Accepted Answer

Inference Latency.. Latency and Token Consumption (or request count) are key indicators of RAG performance and cost efficiency.

Answer

Vector Database Storage Capacity.

Answer

IAM User Count.

Answer

Model Training Loss.

Question 9

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 10

Which configuration in an Amazon Bedrock Knowledge Base is essential for updating the vector index whenever the source files in S3 change?

Accepted Answer

Configure Data Source Syncing.. Syncing is triggered via the 'Sync' operation, but for automation, S3 Event Notifications coupled with a Lambda function or the Bedrock Sync API is required.

Answer

Use AWS Config to monitor bucket changes.

Answer

Set the knowledge base to 'Auto-Refresh' mode.

Answer

Enable S3 Versioning.