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AWS Generative AI Developer Professional Dumps (AIP-C01) - AWS Exam Questions
Last updated on July 22, 2026
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1
Question
An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze 50 to 200 pages of technical documents. Users are experiencing inconsistent responses and receiving truncated outputs when processing documents that exceed the FM's context window limits.
Which solution will resolve this problem?
Which solution will resolve this problem?
Options
A
Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-
level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000
tokens is reached before making inference calls.
level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000
tokens is reached before making inference calls.
B
Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3
sentences. Use the RetrieveAndGenerate API to dynamically select the most relevant chunks based on embedding similarity scores.
sentences. Use the RetrieveAndGenerate API to dynamically select the most relevant chunks based on embedding similarity scores.
C
Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens.
Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.
Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.
D
Create a pre-processing AWS Lambda function that analyzes document token count by using the FM's tokenizer. Configure the Lambda function to split documents into equal segments that fit within
80% of the context window. Configure the Lambda function to process each segment independently
before aggregating the results.
80% of the context window. Configure the Lambda function to process each segment independently
before aggregating the results.
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2
Question
A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data
from the documents. The application must redact personally identifiable information (PII) before
inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant. The company must ensure that the application complies with strict Regional data residency and
auditability requirements. The application must be able to scale to handle 25,000 applications each
day and provide 99.9% availability.
Which combination of solutions will meet these requirements? (Select THREE.)
from the documents. The application must redact personally identifiable information (PII) before
inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant. The company must ensure that the application complies with strict Regional data residency and
auditability requirements. The application must be able to scale to handle 25,000 applications each
day and provide 99.9% availability.
Which combination of solutions will meet these requirements? (Select THREE.)
Select 3
Options
A
Deploy Amazon Textract and Amazon Augmented AI within the same Region to extract relevant data from the scanned documents. Route low-confidence pages to human reviewers.
B
Use AWS Lambda functions to detect and redact PII from submitted documents before inference.
Apply Amazon Bedrock guardrails to prevent inappropriate or unauthorized content in model outputs. Configure Region-specific IAM roles to enforce data residency requirements and to control access to the extracted data.
Apply Amazon Bedrock guardrails to prevent inappropriate or unauthorized content in model outputs. Configure Region-specific IAM roles to enforce data residency requirements and to control access to the extracted data.
C
Use Amazon Kendra and Amazon OpenSearch Service to extract field-level values semantically
from the uploaded documents before inference.
from the uploaded documents before inference.
D
Use AWS Glue Data Quality to validate the structured document data. Use AWS Step Functions to orchestrate a review workflow that includes a prompt engineering step that transforms validated data into optimized prompts before invoking Amazon Bedrock to assess loan applications.
E
Store uploaded documents in Amazon S3 and apply object metadata. Configure IAM policies to store original documents within the same Region as each applicant. Enable object tagging for future audits.
F
Use Amazon SageMaker Clarify to generate fairness and bias reports based on model scoring decisions that Amazon Bedrock makes.
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3
Question
A company has a generative AI (GenAI) application that uses Amazon Bedrock to provide real-time responses to customer queries. The company has noticed intermittent failures with API calls to foundation models (FMs) during peak traffic periods. The company needs a solution to handle transient errors and provide detailed observability into FM performance. The solution must prevent cascading failures during throttling events and provide distributed tracing across service boundaries to identify latency contributors. The solution must also enable correlation of performance issues with specific FM characteristics.
Which solution will meet these requirements?
Which solution will meet these requirements?
Options
A
Implement a custom retry mechanism with a fixed delay of 1 second between retries. Configure Amazon CloudWatch alarms to monitor the application’s error rates and latency metrics.
B
Configure the AWS SDK with standard retry mode and exponential backoff with jitter. Use AWS X- Ray tracing with annotations to identify and filter service components.
C
Implement client-side caching of all FM responses. Add custom logging statements in the application code to record API call durations.
D
Configure the AWS SDK with adaptive retry mode. Use AWS CloudTrail distributed tracing to monitor throttling events.
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4
Question
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
Which solution will meet these requirements?
Options
A
Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.
B
Configure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent
for each user request. Run business logic in the Lambda function to select the appropriate FM for
each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
for each user request. Run business logic in the Lambda function to select the appropriate FM for
each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
C
Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables.
Update the variables when the system switches between models.
Update the variables when the system switches between models.
D
Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.
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5
Question
A publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM. Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant.
Which combination of solutions will meet this requirement? (Select TWO.)
Which combination of solutions will meet this requirement? (Select TWO.)
Select 2
Options
A
Enable model preload upon container startup. Implement dynamic batching to process multiple user requests together in a single inference pass.
B
Switch to a multi-model endpoint. Use lazy loading without request batching.
C
Switch to Amazon SageMaker Asynchronous Inference for all requests. Store requests in an Amazon S3 bucket. Set the minimum number of instances to 0.
D
Select a larger GPU instance type for the SageMaker AI endpoint. Set the minimum number of
instances to 0. Continue to perform per-request processing. Lazily load model weights on the first request.
instances to 0. Continue to perform per-request processing. Lazily load model weights on the first request.
E
Set the minimum number of instances to greater than 0. Enable response streaming.
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