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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Exam Format: | Ordering, Multiple response, Matching, Multiple choice |
| Exam Price: | 300 USD |
| Related Certifications: | AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner |
| Available Languages: | Japanese, Korean, English, Simplified Chinese |
| Passing Score: | 750 (scaled score 100–1000) |
| Exam Duration: | 180 minutes |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Certificate Validity Period: | 3 years |
| Recommended Training: | AWS Certified Generative AI Developer - Professional Exam Guide AWS Skill Builder - Official Training |
| Exam Registration: | AWS Certification Registration Pearson VUE 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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NEW QUESTION # 38
A company uses an AI assistant application to summarize the company's website content and provide information to customers. The company plans to use Amazon Bedrock to give the application access to a foundation model (FM).
The company needs to deploy the AI assistant application to a development environment and a production environment. The solution must integrate the environments with the FM. The company wants to test the effectiveness of various FMs in each environment. The solution must provide product owners with the ability to easily switch between FMs for testing purposes in each environment.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C best satisfies the requirement for flexible FM testing across environments while minimizing operational complexity and aligning with AWS-recommended deployment practices. Amazon Bedrock supports invoking on-demand foundation models through the FoundationModel abstraction, which allows applications to dynamically reference different models without requiring dedicated provisioned capacity. This is ideal for experimentation and A/B testing in both development and production environments.
Using a single AWS CDK application ensures infrastructure consistency and reduces duplication.
Environment-specific configuration, such as selecting different foundation model IDs, can be externalized through parameters, context variables, or environment-specific configuration files. This allows product owners to easily switch between FMs in each environment without modifying application logic.
A single AWS CodePipeline with distinct deployment stages for development and production is an AWS best practice for multi-environment deployments. It enforces consistent build and deployment steps while still allowing environment-level customization. AWS CodeBuild deploy actions enable automated, repeatable deployments, reducing manual errors and improving governance.
Option A increases complexity by introducing multiple pipelines and relies on provisioned models, which are not necessary for FM evaluation and experimentation. Provisioned throughput is better suited for predictable, high-volume production workloads rather than frequent model switching.
Option B creates unnecessary operational overhead by duplicating CDK applications and pipelines, making long-term maintenance more difficult.
Option D directly conflicts with infrastructure-as-code best practices by manually recreating development resources, which increases configuration drift and reduces reliability.
Therefore, Option C provides the most flexible, scalable, and AWS-aligned solution for testing and switching foundation models across development and production environments.
NEW QUESTION # 39
A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarity to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must search documents in English, Spanish, and Portuguese. The application must filter documents by metadata such as publication date, regulatory agency, and document type.
The database stores approximately 10 million document embeddings. To minimize operational overhead, the company wants a solution that minimizes management and maintenance effort while providing low-latency responses for real-time customer interactions.
Which solution will meet these requirements?
Answer: D
Explanation:
Option A is the optimal solution because it provides scalable semantic search, rich metadata filtering, and tight integration with Amazon Bedrock while minimizing operational overhead. Amazon OpenSearch Serverless is designed for high-volume, low-latency search workloads and removes the need to manage clusters, capacity planning, or scaling policies.
With support for vector search and structured metadata filtering, OpenSearch Serverless enables efficient similarity search across 10 million embeddings while applying constraints such as language, publication date, regulatory agency, and document type. This is critical for financial services use cases where relevance and compliance depend on precise filtering.
Integrating OpenSearch Serverless with Amazon Bedrock Knowledge Bases enables a fully managed RAG workflow. The knowledge base handles embedding generation, retrieval, and context assembly, while Amazon Bedrock generates responses using a foundation model. This significantly reduces custom glue code and operational complexity.
Multilingual support is handled at the embedding and retrieval layer, allowing documents in English, Spanish, and Portuguese to be searched semantically without language-specific query logic. OpenSearch's distributed architecture ensures consistent low-latency responses for real-time customer interactions.
Option B increases operational overhead by requiring database tuning and scaling for vector workloads.
Option C does not support advanced metadata filtering, which is a key requirement. Option D introduces unnecessary complexity and is not optimized for large-scale semantic document retrieval.
Therefore, Option A best meets the requirements for performance, scalability, multilingual support, and minimal management effort in an Amazon Bedrock-based RAG application.
NEW QUESTION # 40
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.)
Answer: B,E
Explanation:
The correct answers are A and D because they directly reduce time-to-first-token and stabilize p95 latency for interactive, real-time chat workloads hosted on Amazon SageMaker AI real-time endpoints.
Option D addresses the biggest driver of uneven latency: cold starts and scale-to-zero behavior. By setting the minimum number of instances to greater than 0, the endpoint always has warm capacity and loaded runtime resources, eliminating the first-request penalty that causes users to wait multiple seconds. Enabling response streaming improves perceived latency by returning the first tokens as soon as they are generated rather than waiting for the complete response. This directly targets the abandonment problem described (users leaving after waiting for the first token).
Option A further improves p95 latency and throughput by removing model loading overhead during inference and improving GPU utilization. Preloading model weights during container startup ensures the model is ready before traffic arrives and avoids unpredictable on-demand weight loading. Dynamic batching increases efficiency by grouping compatible requests into a single inference pass, reducing per-request overhead and improving GPU saturation. When tuned properly for interactive workloads, batching can reduce tail latency while preserving responsiveness by enforcing small batch windows.
Option B makes latency worse because setting minimum instances to 0 and lazily loading weights guarantees cold-start delays and unpredictable first-token performance. Option C similarly increases cold-start behavior through lazy loading and offers no batching benefits. Option E is designed for non-interactive workloads and introduces queueing and storage latency, which conflicts with the 800 ms p95 requirement for interactive chat.
Therefore, A and D are the best combination to achieve consistently low p95 latency and fast first-token streaming for a SageMaker-hosted chat assistant.
NEW QUESTION # 41
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A is the only choice that simultaneously addresses all three requirements: (1) higher output consistency for identical inputs, (2) sub-1-second performance, and (3) validated safety controls that block unsafe or hallucinated recommendations.
Provisioned throughput in Amazon Bedrock reserves capacity for the chosen model, which helps stabilize latency and reduces the chance of throttling or variable response times across Regions. This is important for a mobile app with strict latency goals and users distributed across multiple Regions. While provisioned throughput primarily improves performance predictability, it also reduces variability caused by contention during peak demand.
Amazon Bedrock guardrails provide validated safety controls to filter or block unsafe content. Semantic denial rules are appropriate for preventing dangerous brewing guidance (for example, excessively high temperatures) and for reducing hallucinated instructions that violate safety policies. Guardrails can be enforced consistently regardless of prompt-chain complexity, providing a uniform safety layer around the model outputs.
Amazon Bedrock Prompt Management supports controlled prompt versioning and approval workflows. By standardizing prompts, controlling changes, and ensuring the same prompt version is used for identical inputs, the company improves output stability and reduces drift caused by unmanaged prompt edits. Combined with strict configuration control (including fixed inference parameters such as temperature where appropriate), this improves repeatability and increases the likelihood of achieving the 99.5% consistency target.
Option B improves observability and experimentation but does not provide strong safety enforcement or latency stabilization. Option C improves performance through caching and tracing but does not provide validated safety controls and does not directly address cross-Region output consistency. Option D may improve retrieval but does not enforce safety controls or ensure repeatable outputs.
Therefore, Option A best meets the stability, performance, and safety requirements using AWS-native controls.
NEW QUESTION # 42
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?
Answer: B
Explanation:
Option A is the correct solution because it provides proactive, model-aware token management with fine- grained visibility and alerting, which is required for regulated financial workloads. Amazon Bedrock currently exposes token usage metrics after invocation, but it does not natively enforce proactive, model-specific token limits across multiple applications or business units.
By implementing model-specific tokenizers in AWS Lambda, the company can estimate input and output token usage before sending requests to Amazon Bedrock. This enables early detection of requests that are approaching or exceeding model limits and allows the application to block, truncate, or reroute requests proactively rather than reacting to failures.
Publishing token usage metrics to Amazon CloudWatch enables real-time monitoring and alerting at scale, easily supporting more than 5,000 requests per minute. Storing detailed token usage data in Amazon DynamoDB allows the company to attribute usage and costs to specific applications, teams, or business units-an essential requirement for regulatory reporting and internal chargeback.
Option B is incorrect because Amazon Bedrock Guardrails do not currently provide token quota enforcement or proactive token alerts. Option C is reactive and only analyzes failures after they occur. Option D throttles requests but cannot enforce token-based limits or provide per-model cost attribution.
Therefore, Option A best satisfies proactive alerting, scalability, compliance reporting, and cost allocation requirements with acceptable operational effort.
NEW QUESTION # 43
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