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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional (AIP-C01) |
| Exam Number: | AIP-C01 |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 75 scored questions + 10 unscored questions |
| Exam Duration: | 180 minutes |
| Exam Format: | Multiple response, Multiple choice |
| Related Certifications: | AWS Certified Solutions Architect - Associate AWS Certified AI Practitioner AWS Certified Data Engineer - Associate AWS Certified Machine Learning Engineer - Associate |
| Available Languages: | Simplified Chinese, Korean, English, Japanese |
| Passing Score: | 750 (scaled score, range 100–1000) |
| Exam Price: | USD 300 |
| Recommended Training: | Amazon Bedrock Documentation AWS Skill Builder GenAI Learning Path |
| Exam Registration: | Official AWS Certification Page AWS Skill Builder Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite testing center (Pearson VUE) |
| Pre Condition: | Recommended: 2+ years AWS or cloud development experience and 1 year hands-on GenAI implementation experience |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-generative-ai-developer-professional/ |
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NEW QUESTION # 24
A company is building a generative AI (GenAI) application that uses Amazon Bedrock APIs to process complex customer inquiries. During peak usage periods, the application experiences intermittent API timeouts that cause issues such as broken response chunks and delayed data delivery. The application struggles to ensure that prompts remain within token limits when handling complex customer inquiries of varying lengths.
Users have reported truncated inputs and incomplete responses. The company has also observed foundation model (FM) invocation failures.
The company needs a retry strategy that automatically handles transient service errors and prevents overwhelming Amazon Bedrock during peak usage periods. The strategy must also adapt to changing service availability and support response streaming and token-aware request handling.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B best meets all requirements because it combines AWS-recommended resiliency patterns for transient failures with streaming-aware handling and adaptive protection against cascading retries during peak load. When timeouts and throttling occur, naive retries can amplify traffic and worsen outages. Exponential backoff with jitter is the standard AWS best practice because it spreads retry attempts over time, reduces synchronized retry storms, and lowers the probability of repeatedly colliding with service limits.
The requirement also states the strategy must "adapt to changing service availability" and "prevent overwhelming Amazon Bedrock." A circuit breaker pattern directly addresses this by temporarily stopping or reducing retries when failure rates exceed a threshold, allowing the system to degrade gracefully instead of continually hammering the service. This is a key mechanism to prevent cascading failures during throttling events.
Because the application uses response streaming and experiences broken chunks, the retry strategy must be streaming-aware. A streaming response handler that detects chunk delivery timeouts and buffers already received chunks prevents the user from losing progress when a connection drops. Resuming from the last successfully received chunk minimizes redundant generation and reduces additional load on the model compared with restarting the entire stream. This supports better user experience and better service efficiency during intermittent failures.
Token-aware request handling is supported in this architecture because the application can apply token budgeting before invoking the model (for example, trimming or summarizing excessive context) while still preserving streaming output behavior. Option B provides the correct backbone for this by focusing on adaptive control and robust streaming recovery.
Option A is too simplistic and risks retry storms. Option C combines conflicting elements (global token limit, cached completions for streaming) and includes impractical "request only missing chunks" behavior that is not a reliable property of streamed generative output. Option D includes useful ideas (load shedding) but relies on static caps and does not provide as strong adaptive retry control as circuit breaking.
Therefore, Option B is the most correct and operationally safe strategy for peak-load Bedrock streaming workloads.
NEW QUESTION # 25
A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located.
The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers.
Which solution will meet these requirements?
Answer: C
Explanation:
Option B best meets all stated requirements by correctly combining PII protection, evaluation before launch
, and data residency compliance using Amazon Bedrock Guardrails. Amazon Bedrock guardrails provide native sensitive information filtering that operates inline during model invocation, making them well suited for preventing personal data exposure in customer-facing AI assistants.
The requirement to evaluate how effective the AI assistant is at preventing exposure before release is best addressed by using mask mode during development and testing. Mask mode allows responses to be generated while automatically redacting detected personal information, making it easy for developers and reviewers to see where and how PII would have appeared. This provides concrete validation that the guardrail rules are correctly configured without fully blocking responses, which is ideal for quality assurance and pre- production evaluation.
For production, switching the guardrail to block mode ensures that responses containing personal information are fully prevented from being returned to users. This offers the strongest protection and aligns with compliance expectations for customer account data. Block mode is appropriate once confidence in the guardrail configuration has been established during testing.
The data residency requirement is addressed by deploying a copy of the guardrail in each AWS Region where the application operates. Amazon Bedrock guardrails are Region-specific resources, and using Region- local guardrails ensures that inference, filtering, and enforcement all occur within the same Region as the customer data. This avoids cross-Region processing and helps the company comply with regulatory and contractual data residency policies.
Option A and D incorrectly rely on cross-Region guardrails, which can violate data residency constraints.
Option C focuses on topic filtering rather than sensitive information filtering and keeps detect mode enabled in production, which does not actively prevent PII exposure. Therefore, B is the only option that fully satisfies safety, compliance, and evaluation requirements.
NEW QUESTION # 26
A healthcare company runs a multi-step workflow that uses Amazon Bedrock foundation models (FMs) to assist clinicians with making patient clinical decisions. Recent data shows that 25% of decision processes fail to finish. However, the company cannot identify which specific workflow step is causing the failures.
The company needs to implement a monitoring solution to identify the exact failure points in the workflow.
The solution must provide detailed analytics for the completion rates of each workflow step.
Which solution will meet these requirements?
Answer: A
Explanation:
Option C is correct because Amazon Bedrock agent tracing exposes the internal execution path needed to identify precisely where a multi-step workflow fails. When tracing is enabled for InvokeAgent, the response can include structured trace events. AWS documents OrchestrationTrace for orchestration activity and FailureTrace for the reason a particular step failed. Those records can be aggregated to calculate per-step attempts, successes, failures, and completion rates. Option A monitors broad client/server error metrics; even with custom metrics, it does not inherently provide the same structured execution context. Option B uses CloudTrail, which is primarily an API audit trail rather than a step-by-step agent reasoning trace. Option D requires custom EventBridge instrumentation and storage. Native agent trace data therefore provides the most direct evidence for exact failure-point analysis and detailed workflow completion analytics. AWS Documentation
NEW QUESTION # 27
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?
Answer: D
Explanation:
Option C directly addresses the root cause of truncated and inconsistent responses by using AWS- recommended semantic chunking and dynamic retrieval rather than static or sequential chunk processing.
Amazon Bedrock documentation emphasizes that foundation models have fixed context windows and that sending oversized or poorly structured input can lead to truncation, loss of context, and degraded output quality.
Semantic chunking breaks documents based on meaning instead of fixed token counts. By using a breakpoint percentile threshold and sentence buffers, the content remains coherent and semantically complete. This approach reduces the likelihood that important concepts are split across chunks, which is a common cause of inconsistent summarization results.
The RetrieveAndGenerate API is designed specifically to handle large documents that exceed a model's context window. Instead of forcing all content into a single inference call, the API generates embeddings for chunks and dynamically selects only the most relevant chunks based on similarity to the user query. This ensures that the FM receives only high-value context while staying within its context window limits.
Option A is ineffective because chaining chunks sequentially does not align with how FMs process context and risks exceeding context limits or introducing irrelevant information. Option B improves structure but still relies on larger parent chunks, which can lead to inefficiencies when processing very large documents. Option D processes segments independently, which often causes loss of global context and inconsistent summaries.
Therefore, Option C is the most robust, AWS-aligned solution for resolving truncation and consistency issues when processing large technical documents with Amazon Bedrock.
NEW QUESTION # 28
A healthcare company is developing an Amazon Bedrock-based AI clinical assistant. The company needs a solution to track model inputs and outputs so the company can perform offline quality evaluation and regression analysis. The solution must provide centralized, immutable audit records of all model inference and decision events. The company needs a solution that requires minimal runtime overhead so interactive latency remains stable during evaluation and auditing.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C is correct because it combines complete inference capture, immutable retention, and offline evaluation without placing an evaluator in the synchronous request path. Amazon Bedrock model invocation logging can record request data, response data, and metadata to CloudWatch Logs and Amazon S3. S3 Object Lock provides write-once-read-many protection that can prevent retained audit objects from being overwritten or deleted. Bedrock model evaluation jobs can then assess stored or prepared response data offline, supporting regression and quality analysis without adding judge-model latency to every live clinical request. CloudTrail complements the audit record for service API activity. Option A depends on manual reviews. Option B records API activity but not the full inference payloads needed for quality evaluation, and ordinary versioning is not equivalent to WORM retention. Option D scores every request in production, adding runtime overhead.
AWS Documentation
NEW QUESTION # 29
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