AIP-C01PRACTICE QUESTIONS

AIP-C01 practice questions with answers

5 example questions from the AWS Certified Generative AI Developer - Professional question bank, grouped by exam domain. Pick an answer and check it to see the explanation.

Foundation Model Integration, Data Management, and Compliance

Question 1Select 1

null Analyze requirements and design GenAI solutions

A software company is deploying a code-generation assistant that uses a customized foundation model and a proprietary GPU inference container on Amazon SageMaker AI. The container supports incremental token generation. Load testing shows that, with sufficient capacity, the model produces its first token within 1 second and completes a response in 18–25 seconds. The application must display generated text within 2 seconds of a request and continue displaying tokens as they arrive. Two GPU instances can handle normal traffic, but scheduled customer training sessions immediately increase demand to a level that requires 20 instances. Additional instances take approximately 8 minutes to become ready. The company wants to minimize GPU costs outside the scheduled sessions without violating the interactive response requirement during those sessions. Which architecture best meets these requirements?

A. Deploy a SageMaker real-time endpoint with two instances outside training sessions. Schedule capacity increases sufficiently before each session and verify that 20 instances are ready. Use InvokeEndpointWithResponseStream and forward response chunks to the client without buffering the complete response.

B. Deploy a SageMaker real-time endpoint and schedule 20 instances to be ready before each training session, returning to two instances afterward. Use InvokeEndpoint and send its response to the client over a streaming HTTP connection.

C. Deploy a SageMaker asynchronous inference endpoint and schedule 20 instances to be ready before each training session, returning to two instances afterward. Submit requests with InvokeEndpointAsync and notify clients to retrieve results when output objects become available in Amazon S3.

D. Deploy a SageMaker real-time endpoint with a minimum of two instances and target-tracking scaling up to 20 instances. Use InvokeEndpointWithResponseStream and rely on target tracking to add capacity when each training session starts.

Implementation and Integration

Question 1Select 1

null Implement agentic AI solutions and tool integrations

A logistics company uses a generative AI agent to propose customs classifications and supporting explanations for imported products. Before a classification is submitted, two trade specialists independently review the proposal through an Amazon Augmented AI (Amazon A2I) custom task. Each specialist can accept the proposal or provide a corrected classification. If the specialists select different classifications, a senior specialist must examine both responses and make the final selection. Neither a model-generated tie-breaker nor a default classification is permitted. Reviews can take several days. Failed or expired reviews must leave the shipment unsubmitted. The company uses AWS Step Functions Standard Workflows for orchestration and wants to retain the individual reviewer responses for auditing. Which implementation meets these requirements?

A. Start the A2I human loop and retain both specialist responses. If their classifications differ, invoke a foundation model with both explanations and require it to select one of the specialists' classifications. Use the selected classification for submission and send the disagreement to the senior specialist for retrospective review.

B. Start an A2I human loop with three independent reviewers, including the senior specialist. Retain every response and use a Step Functions Choice state to submit the majority classification when at least two responses match. Leave the shipment unsubmitted only when all three classifications differ or the review expires.

C. Start the A2I human loop and use a Standard Workflow with Wait states and bounded status checks. When the loop completes, read and retain its individual worker responses from Amazon S3. Compare the selected classifications in application logic. Continue with the agreed classification, or start a separate adjudication review assigned to the senior specialist when they differ. Submit only after a valid final selection; route failures and deadline expiration to an unsubmitted exception state.

D. Start the A2I human loop and wait until its status is Completed. Treat that status as confirmation that the specialists accepted a classification, submit the agent's proposed classification, and retain the worker responses in Amazon S3 for a subsequent audit.

AI Safety, Security, and Governance

Question 1Select 1

null Implement responsible AI principles

A financial services company uses Amazon Bedrock to generate structured investment recommendations. Company policy requires every recommended product to be approved for the customer's jurisdiction and every response to contain the current jurisdiction-specific disclosure. Jurisdiction is obtained from the customer's authenticated profile, and an authoritative policy service maintains approved products and disclosure text. These rules change independently of application deployments. All customer responses pass through a backend that currently streams model output. The company needs automated, auditable enforcement of these requirements before customers receive recommendations. Which solution BEST meets these requirements?

A. Buffer each complete response and synchronously invoke a Lambda function that validates its schema, checks product identifiers against the authoritative policy for the authenticated customer's jurisdiction, and verifies the required disclosure. Release the response only after validation succeeds; otherwise return a predefined fallback. Record the decision, policy version, and response identifier.

B. Stream responses to customers while asynchronously invoking a Lambda function to validate products and disclosures against the authoritative policy. Record each decision and automatically disable the conversation if the function detects a violation.

C. Buffer responses and synchronously invoke a Lambda function to check the authoritative policy. If the policy service is unavailable, release the response when the model's structured output reports that the products and disclosure are compliant. Log these exceptions for subsequent review.

D. Configure Amazon Bedrock Guardrails with denied topics for restricted products and a word filter containing disclosure text. Use a Lambda function to refresh the guardrail configuration whenever jurisdiction policies change, and continue streaming responses through the guardrail.

Operational Efficiency and Optimization for GenAI Applications

Question 1Select 1

null Implement cost optimization and resource efficiency strategies

A product-catalog application uses Amazon Bedrock to generate a single JSON object containing product attributes and nested arrays of variant objects. The receiving system requires exactly one schema-valid JSON object and does not permit wrapper text or application-specific delimiters. Complete responses require between 300 and 1,800 output tokens. To avoid paying for commentary after the JSON, the developer configured stopSequences as ["}", "]"] and set the maximum output to 2,400 tokens. Production responses frequently end inside the first variant object, causing validation failures and additional model invocations. The inference responses indicate that a configured stop sequence was matched. The selected model supports native schema-constrained JSON output for the catalog's schema. The developer must reduce failed-response retries, preserve all required nested data, and prevent non-JSON commentary without changing the receiving system's format. Which change BEST meets these requirements?

A. Remove both stop sequences, keep plain-text generation, and set the maximum output to the median token count of successful JSON responses.

B. Enable native schema-constrained JSON output, retain both stop sequences, and increase the maximum output to 4,800 tokens.

C. Remove the bracket stop sequence and configure the remaining stop sequence as "\\}$" so that it matches only the final closing brace.

D. Enable native schema-constrained JSON output, remove both configured stop sequences, and retain the 2,400-token maximum.

Testing, Validation, and Troubleshooting

Question 1Select 1

null Implement evaluation systems for GenAI

A logistics company uses deterministic rules and manual review to extract shipment details from documents. The current process costs $630,000 annually, including review labor and losses from extraction errors. A developer evaluates four replacement configurations that use foundation models through Amazon Bedrock. Testing uses the same representative document set and projected production volume. All configurations meet mandatory accuracy, safety, and latency requirements. The evaluation produces the following cost estimates, in thousands of dollars. Setup costs are incurred once at deployment. All other costs cover a full year of production operation. Review-labor reductions represent actual reductions in contractor spending. Error losses are additional costs and are not included in review labor. | Configuration | One-time setup | Annual inference and operations | Annual review labor | Annual expected error losses | |---|---:|---:|---:|---:| | Economy model | 30 | 48 | 420 | 90 | | Premium model | 90 | 120 | 240 | 24 | | Rules-plus-model hybrid | 30 | 84 | 270 | 30 | | Customized model | 180 | 60 | 180 | 36 | The company wants the largest expected net financial benefit relative to the current process during the first 12 months after deployment. There are no other material costs or benefits, and the company does not assign value to benefits after this period. Which recommendation should the developer make?

A. Select the premium model because its lowest expected error losses provide the greatest financial benefit among the eligible configurations.

B. Select the economy model because it has the lowest annual inference and operations expense among the configurations that meet all mandatory requirements.

C. Select the customized model because its lowest recurring annual cost produces the greatest savings once deployment is complete.

D. Select the rules-plus-model hybrid because its combined setup, operating, review, and error costs produce the largest first-year net benefit.

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