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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Optimize and Operationalize a Generative AI Application	30%	- Implement monitoring, logging, and evaluation 1. Implement human-in-the-loop evaluation workflows 2. Implement logging for prompts and responses 3. Evaluate model outputs for quality and bias 4. Monitor model performance and application metrics - Optimize costs and performance 1. Optimize token usage and manage costs 2. Implement auto-scaling for AI workloads 3. Implement caching strategies for frequent queries 4. Optimize model selection and parameter tuning - Implement CI/CD and automation for AI applications 1. Implement testing strategies for generative AI applications 2. Automate deployment of AI models and applications 3. Implement version control for prompts and models 4. Manage model updates and rollback strategies
Topic 2: Plan and Design a Generative AI Application	25%	- Select the appropriate foundation models (FMs) and techniques 1. Evaluate foundation models for the use case 2. Design prompt engineering strategies 3. Select model parameters and configurations 4. Consider fine-tuning vs. retrieval-augmented generation (RAG) - Identify and define the business and technical requirements for a generative AI application 1. Identify constraints and risks 2. Identify the target audience and use cases 3. Define functional and non-functional requirements 4. Determine data requirements and availability - Design the generative AI solution architecture 1. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker) 2. Design the end-to-end solution architecture 3. Integrate with other AWS services (e.g., storage, databases, security) 4. Design for scalability, reliability, and cost-effectiveness
Topic 3: Build and Implement a Generative AI Application	45%	- Implement security, compliance, and responsible AI 1. Implement IAM roles and policies for AI services 2. Implement content filtering and safety mechanisms 3. Implement data encryption and privacy controls 4. Ensure compliance with AI ethics and responsible use - Develop the application using AWS AI services 1. Integrate with knowledge bases 2. Implement retrieval-augmented generation (RAG) patterns 3. Implement inference calls to Amazon Bedrock or other FMs 4. Implement prompt engineering and template management - Implement data preprocessing and vectorization pipelines 1. Ingest and transform data for model consumption 2. Implement embeddings generation using AWS services 3. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora) 4. Implement document chunking and text processing

Section

Weight

Objectives

Topic 1: Optimize and Operationalize a Generative AI Application

30%

- Implement monitoring, logging, and evaluation

1. Implement human-in-the-loop evaluation workflows
2. Implement logging for prompts and responses
3. Evaluate model outputs for quality and bias
4. Monitor model performance and application metrics

- Optimize costs and performance

1. Optimize token usage and manage costs
2. Implement auto-scaling for AI workloads
3. Implement caching strategies for frequent queries
4. Optimize model selection and parameter tuning

- Implement CI/CD and automation for AI applications

1. Implement testing strategies for generative AI applications
2. Automate deployment of AI models and applications
3. Implement version control for prompts and models
4. Manage model updates and rollback strategies

Topic 2: Plan and Design a Generative AI Application

25%

- Select the appropriate foundation models (FMs) and techniques

1. Evaluate foundation models for the use case
2. Design prompt engineering strategies
3. Select model parameters and configurations
4. Consider fine-tuning vs. retrieval-augmented generation (RAG)

- Identify and define the business and technical requirements for a generative AI application

1. Identify constraints and risks
2. Identify the target audience and use cases
3. Define functional and non-functional requirements
4. Determine data requirements and availability

- Design the generative AI solution architecture

1. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker)
2. Design the end-to-end solution architecture
3. Integrate with other AWS services (e.g., storage, databases, security)
4. Design for scalability, reliability, and cost-effectiveness

Topic 3: Build and Implement a Generative AI Application

45%

- Implement security, compliance, and responsible AI

1. Implement IAM roles and policies for AI services
2. Implement content filtering and safety mechanisms
3. Implement data encryption and privacy controls
4. Ensure compliance with AI ethics and responsible use

- Develop the application using AWS AI services

1. Integrate with knowledge bases
2. Implement retrieval-augmented generation (RAG) patterns
3. Implement inference calls to Amazon Bedrock or other FMs
4. Implement prompt engineering and template management

- Implement data preprocessing and vectorization pipelines

1. Ingest and transform data for model consumption
2. Implement embeddings generation using AWS services
3. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora)
4. Implement document chunking and text processing

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q125-Q130):

NEW QUESTION # 125
A GenAI developer is using Amazon Bedrock AgentCore to build an agentic AI application. The application orchestrates multiple foundation models (FMs) across development and production environments. The application experiences intermittent failures during tool invocation phases. Each incident requires an average of 3 hours to diagnose because of complex error patterns in the FM chains.
The GenAI developer needs a solution that can identify AI-specific error patterns in tool invocation chains in less time than the existing manual process. The solution must support collaboration between development and security teams. The solution must provide capabilities to protect sensitive customer data.
Which solution will meet these requirements?

A. Create an Amazon CloudWatch dashboard that displays detailed Amazon Bedrock API metrics.
Integrate the dashboard with Amazon GuardDuty. Use CloudWatch Logs Insights to perform error analysis.
B. Enable Amazon Bedrock AgentCore Observability with AWS Distro for OpenTelemetry instrumentation. Use Amazon CloudWatch Generative AI Observability to perform trace analysis and to monitor tool invocations.
C. Set up AWS X-Ray tracing for all AWS Lambda functions that handle model invocations. Use Amazon OpenSearch Service to create a custom monitoring solution with OpenSearch dashboard visualizations.
D. Use Kiro as the integrated development environment (IDE) to directly access both application codebases and trace logs. Enable real-time analysis of error patterns directly in the IDE by analyzing both application code and trace logs together to understand how the agents orchestrate tool invocations.

Answer: B

Explanation:
Option D is correct because AgentCore Observability is purpose-built for tracing and troubleshooting agentic workloads. AgentCore traces capture the complete execution path, including processing steps, external service calls, tool invocations with inputs and outputs, execution times, decision points, exceptions, recovery attempts, and final responses. CloudWatch Generative AI Observability provides preconfigured views for agent, model, tool, latency, token, and error behavior, allowing development and security teams to investigate failures from shared telemetry instead of correlating several independent logging systems. AWS Distro for OpenTelemetry provides standardized instrumentation for non-runtime agents and related application components. CloudWatch also provides sensitive-data protection capabilities for observability data. Options A and C provide infrastructure-focused monitoring but require more manual correlation and custom dashboards. Option B does not provide the same production-grade centralized AgentCore telemetry. AWS Documentation

NEW QUESTION # 126
A university is building an AI-powered application that includes several sub-applications. The sub- applications include AI assistants, assignment graders, and internal analytics applications. The university is defining and testing multiple prompts by using various foundation models (FMs). The university wants to compare variants of each prompt and choose the variant that yield outputs that are best-suited for specified use cases. The university requires a version control solution for the prompts. The university must be able to test prompt variations and collect audit trails for prompt changes and usage. The solution must also maintain consistency while allowing the prompts to integrate into the main application. Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)

A. Configure Amazon Bedrock intelligent prompt routing.
B. Use Amazon Bedrock Flows to create workflows that combine FMs and AWS services.
C. Store prompts in Amazon S3. Use AWS Step Functions to orchestrate the model interactions and service integrations.
D. Configure AWS Config to record prompt changes. Use AWS CloudTrail to track prompt usage.
E. Use Amazon Bedrock Prompt Management to create versioned prompts. Include parameterized variables for each use case.

Answer: B,E

Explanation:
Amazon Bedrock Prompt Management is the purpose-built service for prompt lifecycle management. it provides native version control , allowing developers to test and compare variants side-by-side. Use of parameterized variables ensures that a single prompt structure can be consistently reused across different sub-applications (assistants vs. graders) while still being tailored to the specific context. To " integrate into the main application " with minimal overhead, Amazon Bedrock Flows provide a managed orchestration layer.
Flows allow developers to link managed prompts with AWS services (like knowledge bases or Lambda functions) without writing complex state-machine logic in Step Functions (Option B). This combination ensures consistent, auditable, and easily deployable prompt assets across the university ' s diverse use cases.

NEW QUESTION # 127
A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?

A. Integrate an Amazon API Gateway HTTP API with an AWS Lambda function to invoke Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic for model timeouts by using Lambda and API Gateway timeout configurations.
B. Connect an Amazon API Gateway WebSocket API to an Amazon ECS service that hosts a containerized inference server. Stream responses by using the WebSocket protocol. Enforce token limits within Amazon ECS. Handle model timeouts by using ECS task lifecycle hooks and restart policies.
C. Connect an Amazon API Gateway HTTP API directly to Amazon Bedrock. Simulate streaming by using client-side polling. Enforce token limits on the frontend. Configure retry behavior by using API Gateway integration settings.
D. Integrate an Amazon API Gateway REST API with an AWS Lambda function that invokes Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic by using Lambda and API Gateway timeout configurations.

Answer: A

Explanation:
Option A is the best solution because it satisfies streaming, token control, and retry requirements while keeping operational overhead low by using fully managed, serverless AWS services. Amazon API Gateway HTTP APIs provide a lightweight, cost-effective front door for APIs and integrate cleanly with AWS Lambda for request processing and security controls.
AWS Lambda response streaming allows the API to begin returning content to the client as soon as partial model output is available, reducing perceived latency and improving user experience for long responses.
Using Lambda as the integration layer also provides a centralized place to enforce token-aware request handling, such as rejecting oversized requests, truncating optional context, or applying consistent limits across users and tenants to manage compute usage.
Retry logic is best handled in the client or integration layer for transient failures such as timeouts and throttling. Lambda can implement controlled retries with exponential backoff and jitter, while API Gateway timeouts help bound request lifetimes and prevent hung connections from consuming resources indefinitely.
Because the model service is managed, the company avoids infrastructure management and focuses only on request shaping, safety, and resiliency behavior.
Option B is not suitable because client-side polling is not true streaming, front-end token enforcement is insecure and inconsistent, and API Gateway does not provide model-aware retry behavior on its own. Option C introduces container hosting and scaling complexity, which increases operational overhead compared to serverless. Option D can work, but REST APIs are generally heavier than HTTP APIs for this pattern and do not reduce overhead compared to Option A.
Therefore, Option A provides the required streaming and resiliency capabilities with the least infrastructure management effort.

NEW QUESTION # 128
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.)

A. Enable model preload upon container startup. Implement dynamic batching to process multiple user requests together in a single inference pass.
B. Switch to a multi-model endpoint. Use lazy loading without request batching.
C. Set the minimum number of instances to greater than 0. Enable response streaming.
D. Select a larger GPU instance type for the SageMaker AI endpoint. Set the minimum number of instances to 0. Continue to perform per-request processing. Lazily load model weights on the first request.
E. Switch to Amazon SageMaker Asynchronous Inference for all requests. Store requests in an Amazon S3 bucket. Set the minimum number of instances to 0.

Answer: A,C

NEW QUESTION # 129
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?

A. Capture dataset lineage in AWS Glue Data Catalog. Store inference payloads in standard Amazon S3 buckets. Use a judge large language model (LLM) to score responses in production for each request.
B. Use AWS CloudTrail to record API calls. Store logs of API calls in an Amazon S3 bucket that has versioning enabled. Use AWS AppConfig feature flags to perform simple online splits without offline evaluation.
C. Use Amazon SageMaker Model Registry to track model versions. Use Amazon Macie to classify artifacts. Write inference outputs to Amazon CloudWatch Logs. Manually perform monthly quality reviews.
D. Send logs for Amazon Bedrock model invocations to Amazon CloudWatch Logs and Amazon S3.
Enable AWS CloudTrail for Amazon Bedrock and Amazon S3 data events. Store logs in an S3 bucket that has Object Lock enabled. Run Amazon Bedrock model evaluations offline.

Answer: D

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 # 130
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