AIP-C01 Practice Test 2 — 25 Questions

Question 1

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

Accepted Answer

InvocationLatency. InvocationLatency and InvocationClientErrors are standard metrics provided for Bedrock in CloudWatch.

Answer

VectorEmbeddingAccuracy

Answer

GPUUtilization

Answer

ModelTrainingLoss

Question 2

A healthcare company requires that all generative AI outputs be filtered for PII (Personally Identifiable Information) before being sent to the end user. Which Amazon Bedrock feature effectively manages this compliance requirement?

Accepted Answer

Amazon Bedrock Guardrails. Guardrails for Amazon Bedrock allows developers to configure PII filtering, profanity filters, and denial topics to ensure compliance.

Answer

AWS CloudTrail logs

Answer

AWS WAF on the API Gateway

Answer

IAM Policy evaluation for Bedrock

Question 3

A developer is preprocessing a large corpus of PDF documents for RAG. To ensure the highest accuracy for multi-modal retrieval tasks, which data strategy is most effective?

Accepted Answer

Applying semantic chunking and adding metadata tags for filtering. Chunking and metadata enrichment allow the retriever to locate specific sections rather than retrieving entire documents, improving precision.

Answer

Converting all text to images before embedding

Answer

Using a fixed-size character count of 500 without overlap

Answer

Concatenating all chunks into one large vector

Question 4

A company is integrating Amazon Bedrock with a private knowledge base. They need to ensure that PII is masked before the retrieved documents are passed to the Foundation Model. Which approach is most effective?

Accepted Answer

Implement an AWS Lambda function with Amazon Comprehend to redact PII during the ingestion process into the Knowledge Base.. Amazon Comprehend can be integrated into the data ingestion pipeline to detect and redact PII before documents are indexed into the Amazon OpenSearch Serverless vector store.

Answer

Configure the Bedrock Knowledge Base to use a custom encryption key for PII filtering.

Answer

Enable Amazon Macie on the S3 bucket and configure Bedrock to ignore flagged objects.

Answer

Use Amazon Bedrock Guardrails to mask PII during the inference request.

Question 5

A developer is troubleshooting a slow retrieval process in a RAG system. What is the most likely cause related to the vector store?

Accepted Answer

The vector index is missing an appropriate HNSW index or is not properly partitioned.. If the vector store lacks an index or the index is not partitioned correctly, search latency increases significantly.

Answer

The embedding model is too small.

Answer

The source documents are too short.

Answer

The Bedrock service is overloaded.

Question 6

A company is building a RAG application using Amazon Bedrock and needs to ensure that sensitive PII is filtered out before it reaches the foundation model. Which feature should be used to achieve this?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails allows users to configure content filters, including PII redaction, to prevent sensitive information from being processed by or returned from foundation models.

Answer

AWS WAF on the Bedrock API endpoint

Answer

Amazon Macie on the S3 bucket housing the source data

Answer

AWS Glue DataBrew

Question 7

Which THREE items must be configured when setting up a new Knowledge Base in Amazon Bedrock?

Accepted Answer

Embedding model selection.. Embedding model, data source, and vector database are the three mandatory components for a Knowledge Base.

Answer

Model evaluation dataset.

Answer

External firewall rules.

Question 8

You are deploying a foundation model on Amazon SageMaker. To optimize inference costs for an application with unpredictable traffic patterns and significant idle periods, which deployment option should you select?

Accepted Answer

SageMaker Serverless Inference. Serverless Inference is the most cost-effective solution for unpredictable traffic as it eliminates the cost of idle instances by automatically scaling resources based on request volume.

Answer

Batch Transform

Answer

Real-time inference with provisioned Auto Scaling instances

Answer

Asynchronous Inference

Question 9

Which TWO types of models are generally available in Amazon Bedrock?

Accepted Answer

Embedding models.. Text generation models and embedding models are the two most common types available in Bedrock.

Answer

Network load balancer configurations.

Answer

Hardware drivers.

Answer

OS kernel images.

Question 10

What is the primary function of the Bedrock 'Model Invocation Logs'?

Accepted Answer

To capture and store prompt/response payloads for auditing.. These logs are used for auditing and debugging by capturing the request and response payloads.

Answer

To throttle incoming requests.

Answer

To perform real-time model retraining.

Answer

To reduce inference costs.

Question 11

Which service should be used to manage user access to Amazon Bedrock models?

Accepted Answer

AWS IAM.. AWS IAM (Identity and Access Management) is the service used to control access to AWS resources, including Bedrock APIs.

Answer

Amazon Route 53.

Answer

Amazon SageMaker.

Answer

AWS Glue.

Question 12

A company is designing a RAG architecture. Which TWO of the following steps are required to successfully link an Amazon S3 source to an Amazon Bedrock Knowledge Base?

Accepted Answer

Configure an IAM role with S3 read access for Bedrock. The process requires selecting the data source (S3) and specifying the embedding model to convert text to vectors.

Answer

Create a Glue Data Catalog for the S3 bucket

Answer

Enable AWS WAF on the S3 bucket

Answer

Create a Lambda function to split PDFs manually

Question 13

What is the primary role of an Embedding Model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors for semantic similarity search.. Embedding models convert text into numerical vectors that represent the semantic meaning of the content for vector search.

Answer

To act as a firewall for the RAG system.

Answer

To compress large files for storage.

Answer

To generate human-readable summaries.

Question 14

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 15

A security auditor requests a report on Bedrock usage. Which TWO of the following AWS services can be used to generate this report?

Accepted Answer

Amazon CloudWatch.. CloudTrail and CloudWatch are the primary services for auditing API usage and logging application events.

Answer

AWS CodePipeline.

Answer

AWS AppConfig.

Answer

AWS Trusted Advisor.

Question 16

When preparing data for a Knowledge Base, why is it recommended to remove PII?

Accepted Answer

To comply with data privacy policies and reduce risk.. Removing PII ensures that the vector index does not store sensitive information, minimizing the risk of data leakage.

Answer

To improve model inference speed.

Answer

Because the models cannot process numbers.

Answer

To reduce the file size.

Question 17

A developer is evaluating model performance for a specific task. Which THREE metrics are commonly used in the Bedrock Model Evaluation console?

Accepted Answer

Inference latency (time to first token).. Accuracy, latency, and toxicity are standard metrics provided in model evaluation frameworks.

Answer

S3 bucket storage size.

Answer

Number of active IAM roles.

Question 18

A company requires their AI to be available even if a regional outage occurs. How do they handle Bedrock model availability?

Accepted Answer

Implement application-level logic to switch regions for inference.. Cross-region inference is not supported by all models, so the application logic must handle failover between regions where the model is available.

Answer

Use an S3 bucket in every region.

Answer

Request a cross-region Bedrock endpoint from AWS Support.

Answer

Enable global AWS region auto-scaling.

Question 19

Which TWO of the following are valid ways to improve the relevance of retrieved documents in a RAG system?

Accepted Answer

Implement metadata filtering to narrow the search space.. Metadata filtering and choosing an optimal chunking strategy are key levers to improve retrieval accuracy.

Answer

Use a larger, more generic embedding model.

Answer

Increase the number of retrieved chunks to 1000.

Answer

Delete all indices and recreate them every hour.

Question 20

Which component in Amazon Bedrock is used to define the structure of the retrieved documents before they are fed into the model?

Accepted Answer

Chunking Strategy.. The 'Chunking Strategy' determines how documents are broken down and structured before being embedded and indexed.

Answer

Prompt Template.

Answer

Vector Database index settings.

Answer

IAM Policy.