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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| Available Languages: | English, Japanese, Simplified Chinese, Korean |
| Passing Score: | 750 (scaled score 100–1000) |
| Exam Duration: | 180 minutes |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Certificate Validity Period: | 3 years |
| Exam Price: | 300 USD |
| Exam Format: | Matching, Multiple choice, Ordering, Multiple response |
| Recommended Training: | AWS Certified Generative AI Developer - Professional Exam Guide AWS Skill Builder - Official Training |
| Exam Registration: | Pearson VUE Registration AWS Certification Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | 2+ years of experience building production-grade applications on AWS or open-source technologies; 1+ year hands-on experience with generative AI implementation; knowledge of AWS compute, storage, networking, security, and deployment tools |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/examguides/ai-professional-01.html |
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120. Frage
A company is building a generative AI (GenAI) application that produces content based on a variety of internal and external data sources. The company wants to ensure that the generated output is fully traceable.
The application must support data source registration and enable metadata tagging to attribute content to its original source. The application must also maintain audit logs of data access and usage throughout the pipeline.
Which solution will meet these requirements?
Antwort: A
Begründung:
Option D is the correct solution because it directly satisfies all three core requirements: data source registration, metadata-based attribution, and end-to-end audit logging, while remaining service-agnostic and scalable across internal and external data sources.
The AWS Glue Data Catalog is the AWS-native service for registering datasets and managing metadata centrally. It supports structured registration of diverse data sources and enables consistent tagging that can be used to attribute generated content back to its original source. This is essential for GenAI applications that combine multiple datasets and must provide traceability for outputs.
Metadata tags applied within the Glue Data Catalog ensure a consistent attribution framework that downstream systems-such as Retrieval Augmented Generation (RAG) pipelines or evaluation systems-can reference without embedding attribution logic directly in application code. This improves maintainability and governance.
AWS CloudTrail provides immutable audit logs of API activity across AWS services, including data access, metadata changes, and pipeline interactions. CloudTrail logs are critical for compliance and regulatory review because they capture who accessed which data, when, and through which service. This satisfies the requirement to maintain audit logs "throughout the pipeline," not just at storage or application layers.
Option A introduces Lake Formation, which is primarily intended for fine-grained data lake permissions and is not required solely for traceability. Option B relies on CloudWatch Logs, which does not provide authoritative audit logging across services. Option C limits audit scope to S3 access and does not register or govern all data sources comprehensively.
Therefore, Option D provides the most complete and least intrusive solution for traceable, auditable GenAI data pipelines.
121. Frage
A company developed a multimodal content analysis application by using Amazon Bedrock. The application routes different content types (text, images, and code) to specialized foundation models (FMs).
The application needs to handle multiple types of routing decisions. Simple routing based on file extension must have minimal latency. Complex routing based on content semantics requires analysis before FM selection. The application must provide detailed history and support fallback options when primary FMs fail.
Which solution will meet these requirements?
Antwort: A
Begründung:
Option B is the most appropriate solution because it directly aligns with AWS-recommended architectural patterns for building scalable, observable, and resilient generative AI applications on Amazon Bedrock. The requirements clearly distinguish between simple and complex routing decisions, and this option addresses both in an optimal way.
Simple routing based on file extension is latency sensitive. Handling this logic directly in the application code avoids unnecessary orchestration, state transitions, and service calls. This approach ensures that straightforward requests, such as routing images to vision-capable foundation models or text files to language models, are processed with minimal overhead and maximum performance.
For complex routing based on content semantics, AWS Step Functions is specifically designed for multi-step workflows that require analysis, branching logic, and error handling. Semantic routing often requires inspecting meaning, intent, or structure before selecting the appropriate foundation model. Step Functions enables this by orchestrating analysis steps and applying conditional logic to determine the correct model to invoke using the Amazon Bedrock InvokeModel API.
A key requirement is detailed execution history. Step Functions provides built-in execution tracing, including state inputs, outputs, and error details, which is essential for auditing, debugging, and compliance.
Additionally, Step Functions supports native retry and catch mechanisms, allowing the workflow to automatically fall back to alternate foundation models if a primary model invocation fails. This directly satisfies the fallback requirement without introducing excessive custom code.
The other options lack one or more critical capabilities. Lambda-only logic lacks deep observability and structured fallback handling, SQS introduces additional latency and limited workflow visibility, and multiple coordinated workflows increase architectural complexity without added benefit.
122. Frage
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.)
Antwort: C,E
123. Frage
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?
Antwort: B
Begründung:
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.
124. Frage
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution.
The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units.
Which solution will meet these requirements?
Antwort: D
125. Frage
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