Focus your preparation on:
Domain 1: Foundation Model Integration, Data Management, and Compliance
Domain 2: Implementation and Integration
Domain 3: AI Safety, Security, and Governance
Domain 4: Operational Efficiency and Optimization
Domain 5: Testing, Validation, and Troubleshooting
Braindump2go has assembled to take you through 194 Q&As to your AIP-C01 Exam preparation: https://www.braindump2go.com/aip-c01.html
QUESTION 1
A financial services company is building a customer support application that retrieves relevant
financial regulation documents from a database based on semantic similarity to user queries. The
application must integrate with Amazon Bedrock to generate responses. The application must
search documents in English, Spanish, and Portuguese. The application must filter documents by
metadata such as publication date, regulatory agency, and document type.
The database stores approximately 10 million document embeddings. To minimize operational
overhead, the company wants a solution that minimizes management and maintenance effort
while providing low-latency responses for real-time customer interactions.
Which solution will meet these requirements?
A. Use Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering.
Integrate with Amazon Bedrock Knowledge Bases to enable Retrieval Augmented Generation
(RAG) using an Anthropic Claude foundation model.
B. Deploy an Amazon Aurora PostgreSQL database with the pgvector extension. Store embeddings
and metadata in tables. Use SQL queries for similarity search and send results to Amazon
Bedrock for response generation.
C. Use Amazon S3 Vectors to configure a vector index and non-filterable metadata fields. Integrate
S3 Vectors with Amazon Bedrock for RAG.
D. Set up an Amazon Neptune Analytics database with a vector index. Use graph-based retrieval
and Amazon Bedrock for response generation.
Answer: A
QUESTION 2
An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application.
The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce
detailed product descriptions. The workflow consists of three sequential states: a description
generator, a technical specifications validator, and a brand voice consistency checker. Each state
produces intermediate reasoning traces and outputs that are passed to the next state. The
application uses an Amazon S3 bucket for process storage and to store outputs.
During testing, the company discovers that outputs between Step Functions states frequently
exceed the 256 KB quota and cause workflow failures. A GenAI Developer needs to revise the
application architecture to efficiently handle the Step Functions 256 KB quota and maintain
workflow observability. The revised architecture must preserve the existing multi-agent reasoning
and acting (ReAct) pattern.
Which solution will meet these requirements with the LEAST operational overhead?
A. Store intermediate outputs in Amazon DynamoDB. Pass only references between states. Create
a Map state that retrieves the complete data from DynamoDB when required for each agent's
processing step.
B. Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameters for
large outputs. Use the ResultPath and ResultSelector fields to route S3 references between the
agent steps while maintaining the sequential validation workflow.
C. Use AWS Lambda functions to compress outputs to less than 256 KB before each agent state.
Configure each agent task to decompress outputs before processing and to compress results
before passing them to the next state.
D. Configure a separate Step Functions state machine to handle each agent's processing. Use
Amazon EventBridge to coordinate the execution flow between state machines. Use S3
references for the outputs as event data.
Answer: B
QUESTION 3
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by
using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs
into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized
roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs,
slow inference during the retrieval step, and unsafe recommendations such as brewing at
excessively high temperatures. The company must improve the stability of outputs for repeated
inputs. The company must also improve app performance and the safety of the app's outputs.
The updated solution must ensure 99.5% output consistency for identical inputs and achieve
inference latency of less than 1 second. The solution must also block unsafe or hallucinated
recommendations by using validated safety controls.
Which solution will meet these requirements?
A. Deploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply
Amazon Bedrock guardrails with semantic denial rules to block unsafe outputs. Use Amazon
Bedrock Prompt Management to manage prompts by using approval workflows.
B. Use Amazon Bedrock Agents to manage chaining. Log model inputs and outputs to Amazon
CloudWatch Logs. Use logs from CloudWatch to perform A/B testing for prompt versions.
C. Cache prompt results in Amazon ElastiCache. Use AWS Lambda functions to pre-process
metadata and to trace end-to-end latency. Use AWS X-Ray to identify and remediate
performance bottlenecks.
D. Use Amazon Kendra to improve roast log retrieval accuracy. Store normalized prompt metadata
within Amazon DynamoDB. Use AWS Step Functions to orchestrate multi-step prompts.
Answer: A
QUESTION 4
A media company must use Amazon Bedrock to implement a robust governance process for AIgenerated content. The company needs to manage hundreds of prompt templates. Multiple
teams use the templates across multiple AWS Regions to generate content. The solution must
provide version control with approval workflows that include notifications for pending reviews. The
solution must also provide detailed audit trails that document prompt activities and consistent
prompt parameterization to enforce quality standards. Which solution will meet these
requirements?
A. Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch dashboards to
display prompt usage metrics. Store approval status in Amazon DynamoDB. Use AWS Lambda
functions to enforce approvals.
B. Use Amazon Bedrock Prompt Management to implement version control. Configure AWS
CloudTrail for audit logging. Use AWS Identity and Access Management policies to control
approval permissions. Create parameterized prompt templates by specifying variables.
C. Use AWS Step Functions to create an approval workflow. Store prompts in Amazon S3. Use tags
to implement version control. Use Amazon EventBridge to send notifications.
D. Deploy Amazon SageMaker Canvas with prompt templates stored in Amazon S3. Use AWS
CloudFormation for version control. Use AWS Config to enforce approval policies.
Answer: B
QUESTION 5
A company is developing a generative AI (GenAI) application that uses Amazon Bedrock
foundation models. The application has several custom tool integrations. The application has
experienced unexpected token consumption surges despite consistent user traffic.
The company needs a solution that uses Amazon Bedrock model invocation logging to monitor
InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in
tool usage and identify which specific tool integrations cause abnormal token consumption. The
solution must also automatically adjust thresholds as traffic patterns change.
Which solution will meet these requirements?
A. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch
dashboards for token metrics. Configure static CloudWatch alarms with fixed thresholds for each
tool integration.
B. Store model invocation logs in Amazon S3. Use AWS Glue and Amazon Athena to analyze token
usage trends.
C. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch metric
filters to extract tool-specific invocation patterns. Apply CloudWatch anomaly detection alarms
that automatically adjust baselines for each tool's token metrics.
D. Store model invocation logs in an Amazon S3 bucket. Use AWS Lambda to process logs in real
time. Manually update CloudWatch alarm thresholds based on trends identified by the Lambda
function.
Answer: C
QUESTION 6
A financial services company needs to pre-process unstructured data such as customer
transcripts, financial reports, and documentation. The company stores the unstructured data in
Amazon S3 to support an Amazon Bedrock application.
The company must validate data quality, create auditable metadata, monitor data metrics, and
customize text chunking to optimize foundation model (FM) performance.
W
hich solution will meet these requirements with the LEAST development effort?
A. Use Amazon SageMaker Data Wrangler to create a data flow. Configure Amazon CloudWatch
metrics and alarms to monitor data quality. Use a custom AWS Lambda function to pre-process
the data. Load processed data into Amazon Bedrock.
B. Set up an AWS Glue crawler to catalog data sources. Create AWS Glue ETL jobs to run custom
transformation scripts. Use AWS Glue Data Quality to validate and monitor data quality. Load
processed data into Amazon Bedrock.
C. Use Amazon Comprehend to extract entities. Create an AWS Lambda function to chunk text. Run
Amazon Athena to query and validate data quality. Load processed data into Amazon Bedrock.
D. Create an AWS Step Functions workflow to orchestrate data pre-processing tasks. Run custom
code on Amazon EC2 instances. Use Amazon SageMaker Model Monitor to monitor data quality.
Load processed data into Amazon Bedrock.
Answer: B
QUESTION 7
An ecommerce company is developing a generative AI (GenAI) solution that uses Amazon
Bedrock with Anthropic Claude to recommend products to customers. Customers report that
some recommended products are not available for sale or are not relevant. Customers also report
long response times for some recommendations.
The company confirms that most customer interactions are unique and that the solution
recommends products not present in the product catalog.
Which solution will meet this requirement?
A. Increase grounding within Amazon Bedrock Guardrails. Enable automated reasoning checks. Set
up provisioned throughput.
B. Use prompt engineering to restrict model responses to relevant products. Use streaming
inference to reduce perceived latency.
C. Create an Amazon Bedrock Knowledge Bases and implement Retrieval Augmented Generation
(RAG). Set the PerformanceConfigLatency parameter to optimized.
D. Store product catalog data in Amazon OpenSearch Service. Validate model recommendations
against the catalog. Use Amazon DynamoDB for response caching.
Answer: C
QUESTION 8
A financial services company is developing a real-time generative AI (GenAI) assistant to support
human call center agents. The GenAI assistant must transcribe live customer speech, analyze
context, and provide incremental suggestions to call center agents while a customer is still
speaking. To preserve responsiveness, the GenAI assistant must maintain end-to-end latency
under 1 second from speech to initial response display. The architecture must use only managed
AWS services and must support bidirectional streaming to ensure that call center agents receive
updates in real time. Which solution will meet these requirements?
A. Use Amazon Transcribe streaming to transcribe calls. Pass the text to Amazon Comprehend for
sentiment analysis. Feed the results to Anthropic Claude on Amazon Bedrock by using the
InvokeModel API. Store results in Amazon DynamoDB. Use a WebSocket API to display the
results.
B. Use Amazon Transcribe streaming with partial results enabled to deliver fragments of transcribed
text before customers finish speaking. Forward text fragments to Amazon Bedrock by using the
InvokeModelWithResponseStream API. Stream responses to call center agents through an
Amazon API Gateway WebSocket API.
C. Use Amazon Transcribe batch processing to convert calls to text. Pass complete transcripts to
Anthropic Claude on Amazon Bedrock by using the ConverseStream API. Return responses
through an Amazon Lex chatbot interface.
D. Use the Amazon Transcribe streaming API with an AWS Lambda function to transcribe each
audio segment. Call the Amazon Titan Embeddings model on Amazon Bedrock by using the
InvokeModel API. Publish results to Amazon SNS.
Answer: B