AWS · Free Practice Questions · Last reviewed May 2026
30real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
You are deploying a Generative AI application using Amazon SageMaker JumpStart. You need to ensure that the model endpoint is protected against prompt injection attacks. Which approach is most effective?
Encrypt the model weights with a CMK
Enable Amazon Inspector on the endpoint
Use a prompt-based filter system that detects malicious intent via a secondary validation model
Using a secondary model or Guardrails to evaluate input intent is the standard defense against prompt injection.
Disable the API Gateway execute-api method
An enterprise requires that all Generative AI model outputs are logged for compliance, but logs must not contain any PII. How can this be achieved in an AWS environment using Amazon Bedrock?
Write a Lambda function to strip logs before writing to S3
Enable Bedrock Model Invocation Logging with Guardrails PII redaction enabled
This is the native method to log inference data while ensuring PII is stripped by the Guardrail.
Use IAM policies to prevent logs from being written
Use AWS CloudTrail to log API calls and enable redaction in CloudWatch
A developer wants to prevent the Generative AI application from producing content related to hate speech or violence. What is the most efficient configuration in Amazon Bedrock?
Create a custom fine-tuned model
Implement Amazon Bedrock Guardrails with content filters
Guardrails are the purpose-built tool for defining safety thresholds for output content.
Use AWS Config rules to monitor the model
Add a system prompt to the application code
A company is using RAG with Amazon Bedrock Knowledge Bases. They need to ensure that users can only retrieve documents they are authorized to see. How should this be implemented?
Encrypt the documents with different KMS keys
Create separate Knowledge Bases for every user
Apply IAM policies to the S3 bucket
Use the filters attribute in the Retrieve API call based on document metadata
The filter functionality in Retrieve allows restricting results based on metadata attributes like 'user_department'.
To ensure compliance with data residency requirements, you must restrict which AWS regions your Generative AI application can process data. What is the most effective way to enforce this?
Service Control Policies (SCPs)
SCPs are the standard mechanism to enforce organizational boundaries across AWS accounts.
VPC Endpoint policies
IAM policies for all users
CloudFront geographic restrictions
Your organization needs to ensure that only authorized IAM users can access specific foundation models in Bedrock. Which configuration is required?
Attach an IAM policy with bedrock:InvokeModel allowing access to specific model ARNs
This is the correct approach to restrict access to specific models using IAM.
Enable AWS Organizations SCPs only
Use Amazon Cognito user pools
Use Resource-Based Policies on the foundation model
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Practice this domainWhen using the Amazon Bedrock Converse API, which parameter enables the model to perform tool use by providing a definition of available functions?
inferenceConfig
toolConfig
toolConfig is the correct parameter to define tools.
system
additionalModelRequestFields
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?
Configure Subnets and Security Groups in the Model creation
VPC configuration is required for container network access.
Set the IAM role to 'AdministratorAccess'
Update the JumpStart model Dockerfile
Enable AWS PrivateLink on the S3 bucket
You are designing an Agentic AI solution using Bedrock Agents. Which THREE components must you configure to enable the agent to interact with an external API?
Knowledge Base
Action Group
Required to group actions.
OpenAPI Schema
Defines the API structure for the agent.
Lambda Function
The backend execution logic for the API.
Guardrail
When using Bedrock Knowledge Bases, which vector database is integrated natively to support managed RAG workflows?
Amazon OpenSearch Serverless
OpenSearch Serverless is the primary native vector store for Bedrock.
Amazon DynamoDB
Amazon ElastiCache
Amazon RDS for PostgreSQL
You are integrating a SageMaker JumpStart model via the InvokeEndpoint API. The model is failing to process images due to a schema mismatch. Which format does the JumpStart image input expect by default for standard vision models?
Base64 encoded string in the JSON input field
Standard JumpStart vision models require base64 encoding.
A URL pointing to an S3 object
Multi-part form data
Raw binary stream in the request body
An application uses Amazon Bedrock InvokeModel to generate text. You notice that the output length is consistently truncated. Which parameter should you adjust to ensure the full response is captured?
Increase the max_tokens parameter
Increasing max_tokens allows the model to generate more tokens.
Increase the temperature parameter
Increase the top_p parameter
Decrease the stop_sequences parameter
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Practice this domainYou 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?
SageMaker Serverless Inference
Serverless scales automatically and charges only for the duration of request processing.
Batch Transform
Real-time inference with provisioned Auto Scaling instances
Asynchronous Inference
You need to ensure that your SageMaker inference endpoint does not exceed a specific monthly budget. Which AWS feature allows you to set up notifications when spending thresholds are crossed?
SageMaker Cost Explorer
CloudWatch Alarms on billing metrics
AWS Budgets
AWS Budgets allows proactive threshold monitoring and alerting.
AWS Cost and Usage Reports
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?
Using spot instances for all endpoints
Configuring Application Auto Scaling with scheduled actions
Scheduled actions can change the instance count based on a recurring timeline.
Deactivating the endpoint manually
Updating the endpoint configuration
An application using Amazon Bedrock is experiencing high latency. You need to monitor the 'ModelInvocationLatency' metric to identify performance bottlenecks. Which AWS service provides the primary integration for this telemetry?
AWS CloudTrail
AWS X-Ray
Amazon CloudWatch
Bedrock publishes standard operational metrics directly to CloudWatch.
Amazon OpenSearch Service
You are tuning a Large Language Model on SageMaker and notice that GPU memory utilization is consistently low despite high latency. Which action is most appropriate to optimize performance?
Increasing the number of instances
Switching to a larger instance type
Moving the model to CPU instances
Implementing dynamic batching in your inference handler
Dynamic batching groups requests to maximize GPU utilization and reduce latency.
To track the resource usage of a SageMaker training job, you should look at which CloudWatch metric?
GPUUtilization
This metric tracks the hardware performance of the training node.
RequestCount
ModelLatency
Invocations
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Practice this domainWhich TWO factors are critical when choosing a model for a RAG architecture that requires high grounding accuracy?
The model's training date.
The model's ability to generate images.
The model's support for citation and source attribution.
Citation capability is crucial for verifying the grounding of the model response.
The number of parameters in the model.
The context window size of the foundation model.
Larger context windows allow for more retrieved information to be processed.
A developer is designing a RAG system and wants to ensure high-quality retrieval. Which THREE techniques should be implemented to improve semantic search relevance?
Using a reranking model to score retrieved documents
Reranking improves the relevance of the retrieved document set before generation.
Applying semantic chunking based on document structure
Semantic chunking keeps related information together better than fixed-size chunking.
Increasing the vector dimension size to maximum
Implementing hybrid search combining keyword and vector search
Hybrid search captures both lexical matches and semantic meaning.
Using only fixed-size overlap chunking
A developer wants to choose a foundation model in Amazon Bedrock that offers the best balance of reasoning capability and latency for a customer support chatbot. Which tool should the developer use to evaluate models?
Amazon CloudWatch Metrics for Bedrock.
AWS Glue DataBrew.
Bedrock Model Evaluation jobs.
This service is specifically designed to evaluate and compare foundation models.
Amazon SageMaker Model Monitor.
An organization is deploying a RAG architecture using Amazon Bedrock and Amazon OpenSearch Serverless. The security team requires that all data remain within the VPC. How should the developer configure the integration?
Configure the Knowledge Base to use an S3 Gateway Endpoint only.
Deploy the Bedrock Knowledge Base using a PrivateLink interface endpoint for Bedrock and a VPC endpoint for OpenSearch Serverless.
PrivateLink ensures the traffic never traverses the public internet, satisfying security requirements for sensitive data.
Use a NAT Gateway to route all traffic to the Bedrock service.
Use an API Gateway with a WAF to restrict access to the vector store.
A company is building a RAG application using Amazon Bedrock and needs to ensure that the data ingested into the knowledge base is filtered based on user identity. Which approach should the developer use?
Use Amazon Bedrock Knowledge Base metadata filtering during the retrieval phase to apply access controls.
Metadata filtering allows precise control over which documents are retrieved during query time based on user-specific attributes.
Use Lambda authorizers to filter the retrieved results after they are returned by the model.
Create separate vector indexes for every user role within the Knowledge Base configuration.
Implement fine-grained access control using IAM policies on the S3 bucket containing the source data.
A healthcare organization must ensure that PII is removed from documents before they are stored in an Amazon Bedrock Knowledge Base. Which THREE actions should the developer take?
Manually encrypt all files using AWS KMS before uploading to S3.
Configure the Knowledge Base to use a private VPC endpoint to prevent data exfiltration.
VPC endpoints ensure data traverses the AWS network, which is a compliance requirement.
Enable Amazon Bedrock Data Protection policies to automatically strip PII during vectorization.
Use Amazon Comprehend to detect and redact PII in the source documents before ingestion.
Amazon Comprehend provides PII detection and redaction capabilities.
Implement an AWS Glue ETL job to sanitize data during the ingestion pipeline.
Glue is the standard tool for cleaning data at scale before storage.
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Practice this domainAn application deployed on AWS Lambda is receiving 429 Too Many Requests errors when calling Amazon Bedrock. The developer has verified that account-level quotas have not been reached. What is the most likely cause?
The VPC endpoint policy is blocking traffic to the Bedrock runtime service.
The application is exceeding the tokens-per-minute (TPM) limit for the specific model in that AWS Region.
Each model has specific throughput limits that, when exceeded, trigger 429 errors.
The Lambda function timeout is set too low for the model response time.
The IAM role is missing the bedrock:InvokeModelWithResponseStream permission.
A developer is using Amazon Bedrock to build a customer support chatbot. The model is hallucinating frequently when retrieving internal company knowledge. Which evaluation methodology should the developer implement to quantify the ground truth accuracy of the RAG pipeline?
Implement a RAGAS-based evaluation pipeline to measure faithfulness and answer relevance.
RAGAS metrics are industry standard for measuring the effectiveness of the retrieval and generation components.
Increase the temperature setting to 1.0 to encourage more creative responses.
Use Amazon CloudWatch logs to calculate the latency of every inference call.
Run a standard BLEU score evaluation against the model's training data.
A developer is creating a test suite for an LLM application. Which THREE of the following metrics are commonly used to evaluate RAG system performance?
Answer Relevance
Measures how relevant the answer is to the original query.
Context Precision
Measures if the relevant items are ranked higher in the retrieval.
Model Training Loss
Faithfulness
Measures if the answer is derived solely from the retrieved context.
Inference Latency
Which THREE of the following are valid ways to monitor Amazon Bedrock model usage?
AWS CloudTrail logs.
Tracks API calls and administrative actions.
Amazon CloudWatch metrics.
Standard service for monitoring request counts and latency.
AWS X-Ray service map.
AWS Billing and Cost Management reports.
Provides detailed usage cost and token usage data.
Amazon S3 bucket logging.
A developer is troubleshooting an Amazon Bedrock Agent that is frequently hitting errors. Which TWO of the following steps should be taken to isolate the issue?
Restart the Bedrock service.
Increase the model's temperature to 1.0.
Check CloudWatch Logs for the associated Action Group Lambda functions.
If the agent is failing during tool execution, the error is likely in the Lambda function code.
Review the Agent's 'Trace' output to identify the failure point.
The trace shows the exact reasoning step where the agent failed.
Re-train the underlying base model.
A developer is testing an application and finds that the model output is too verbose. Which TWO of the following parameters or techniques can help control the response length?
Reduce the 'max_tokens' parameter.
This sets a hard limit on the number of generated tokens.
Increase the temperature.
Enable Guardrails to truncate output.
Use a different model family.
Update the system prompt to include 'be concise'.
System prompts are effective at controlling the style and verbosity of the response.
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Practice this domainThe AIP-C01 exam has 200 questions and must be completed in 120 minutes. The passing score is 700/1000.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 5 domains: AI Safety Security And Governance, Implementation And Integration, Operational Efficiency And Optimization, Foundation Model Integration Data And Compliance, Testing Validation And Troubleshooting. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official AWS AIP-C01 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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