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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

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

- Implement data preprocessing and vectorization pipelines

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

- Implement security, compliance, and responsible AI

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

Topic 2: Optimize and Operationalize a Generative AI Application

30%

- Optimize costs and performance

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

- Implement monitoring, logging, and evaluation

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

- Implement CI/CD and automation for AI applications

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

Topic 3: Plan and Design a Generative AI Application

25%

- Select the appropriate foundation models (FMs) and techniques

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

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

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

- Design the generative AI solution architecture

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q141-Q146):

NEW QUESTION # 141
A financial services company is developing an AI-powered search assistant application to help investment advisors quickly retrieve investment data. The application runs as an AWS Lambda function. The company is using Amazon Bedrock to develop the application by using an Amazon Bedrock knowledge base that uses Amazon OpenSearch Serverless as its data source. The application agent must manage collections at scale by automatically assigning access permissions to collections and indexes that match a specific pattern. The company uses Amazon Bedrock tools to test the knowledge base. The knowledge base sync process finishes successfully. However, the test reveals a 400 Bad Authorization error from the BedrockAgentRuntime API and a 403 Forbidden error when the test attempts to access OpenSearch Serverless. The company must resolve the permissions issues. Which combination of solutions will meet this requirement? (Select TWO.)

A. Create an OpenSearch Serverless data access policy that includes pattern-based resource rules.
B. Update the Lambda function execution role to include the bedrock:InvokeAgent permission. Add the aoss:APIAccessAll permission to the Lambda execution role.
C. Configure AWS Secrets Manager to store OpenSearch Serverless credentials. Grant the Lambda function access to retrieve the credentials.
D. Enable IAM authentication for the OpenSearch Serverless domain. Add the es:ESHttp* permission to the Lambda function execution role.
E. Configure a VPC endpoint policy for OpenSearch Serverless. Add the endpoint to the Lambda function
' s VPC configuration.

Answer: A,B

Explanation:
The errors described indicate missing permissions at both the application orchestration and data access levels.
The 400 Bad Authorization from BedrockAgentRuntime indicates the Lambda execution role lacks the identity permission to invoke the agent; adding bedrock:InvokeAgent and aoss:APIAccessAll (which allows the principal to interact with OpenSearch Serverless APIs) is necessary. The 403 Forbidden error from OpenSearch Serverless specifically relates to data-plane permissions. Unlike traditional OpenSearch, Serverless uses data access policies . To " manage collections at scale " automatically, a policy must be created that uses pattern-based resource rules (e.g., matching a prefix), ensuring that as new collections or indexes are created, the required principals (the Lambda role and the Bedrock service role) are granted the necessary access without manual policy updates for every new resource.

NEW QUESTION # 142
A healthcare company creates a custom foundation model (FM) that uses a proprietary architecture to summarize and answer questions about sensitive patient records and conversations. To comply with regulations, the company must ensure confidentiality by implementing extensive monitoring and controls. The company must verify the accuracy of the FM by checking prompts and responses for hallucinations.
Which solution will meet these requirements?

A. Use Amazon SageMaker Serverless Inference to host the model. Configure Amazon Bedrock guardrails that apply contextual grounding checks with high thresholds for grounding and relevance. Use custom application code that routes prompts and responses through the guardrails.
B. Use the Custom Model Import feature in Amazon Bedrock to import the FM. Configure Amazon Bedrock guardrails that apply content filters with high thresholds for grounding and relevance.
C. Use the Custom Model Import feature in Amazon Bedrock to import the FM. Configure AWS HealthScribe to apply contextual grounding check rules to comply with regulatory requirements.
D. Use Amazon SageMaker JumpStart to import the FM to Amazon Bedrock. Configure Amazon Bedrock guardrails that apply content filters with high thresholds for grounding and relevance.

Answer: A

Explanation:
Option B is correct under current AWS capabilities. Amazon Bedrock Custom Model Import accepts specified supported model architectures; it is not a mechanism for importing an arbitrary proprietary architecture. SageMaker inference supports bringing a custom container, making it appropriate when custom model architecture or inference code must be preserved. Amazon Bedrock Guardrails can be applied independently of Bedrock-hosted foundation models through the ApplyGuardrail API, including to output from third-party or externally hosted models. Contextual grounding checks calculate grounding and relevance scores and can block responses below configured thresholds, directly addressing hallucination detection.
Options A and C incorrectly use ordinary content filters for grounding and relevance, which are separate contextual-grounding capabilities. Option D incorrectly assigns contextual-grounding functionality to AWS HealthScribe rather than Amazon Bedrock Guardrails. AWS Documentation

NEW QUESTION # 143
A retail company is using Amazon Bedrock to develop a customer service AI assistant. Analysis shows that
70% of customer inquiries are simple product questions that a smaller model can effectively handle. However,
30% of inquiries are complex return policy questions that require advanced reasoning.
The company wants to implement a cost-effective model selection framework to automatically route customer inquiries to appropriate models based on inquiry complexity. The framework must maintain high customer satisfaction and minimize response latency.
Which solution will meet these requirements with the LEAST implementation effort?

A. Implement a single-model solution that uses an Amazon Bedrock mid-sized foundation model (FM) with on-demand pricing. Include special instructions in model prompts to handle both simple and complex inquiries by using the same model.
B. Create separate Amazon Bedrock endpoints for simple and complex inquiries. Implement a rule-based routing system based on keyword detection. Use on-demand pricing for the smaller model and provisioned throughput for the larger model.
C. Create a multi-stage architecture that uses a small foundation model (FM) to classify the complexity of each inquiry. Route simple inquiries to a smaller, more cost-effective model. Route complex inquiries to a larger, more capable model. Use AWS Lambda functions to handle routing logic.
D. Use Amazon Bedrock intelligent prompt routing to automatically analyze inquiries. Route simple product inquiries to smaller models and route complex return policy inquiries to more capable larger models.

Answer: D

Explanation:
Option B is the correct solution because it leverages native Amazon Bedrock intelligent prompt routing, which is specifically designed to reduce cost and complexity in multi-model GenAI architectures. Intelligent prompt routing automatically analyzes incoming prompts and selects the most appropriate foundation model based on prompt characteristics and complexity-without requiring custom classification logic or orchestration code.
This approach directly meets the requirement for least implementation effort. The company does not need to deploy additional Lambda functions, maintain routing rules, or manage separate classification stages. Routing decisions are handled by Bedrock, which simplifies architecture and reduces operational risk.
By routing the majority (70%) of simple product inquiries to smaller, lower-cost models, the company minimizes inference cost and latency. More complex return policy inquiries are automatically routed to larger models that provide better reasoning capabilities, preserving response quality and customer satisfaction.
Because routing is handled inline by Bedrock, response latency remains low compared to multi-stage architectures that require an additional classification model call before inference. This is critical for customer service scenarios where responsiveness directly impacts satisfaction.
Option A introduces additional inference steps and custom logic. Option C increases cost by overusing a mid- sized model for all queries. Option D relies on brittle keyword rules and increases operational overhead through endpoint management.
Therefore, Option B delivers the optimal balance of cost efficiency, performance, and simplicity for dynamic model selection in Amazon Bedrock.

NEW QUESTION # 144
An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians.
The company uses Amazon Kinesis Data Streams to collect the elevator sensor data.
New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes.
Which solution will meet these requirements?

A. Create a custom approval workflow by using AWS Lambda functions and Amazon SQS queues for human review of AI recommendations. Store all review decisions in Amazon DynamoDB for audit purposes.
B. Create an AWS Step Functions workflow that has a human approval step that uses the waitForTaskToken API to pause execution. After a human technician completes a review, use an AWS Lambda function to call the SendTaskSuccess API with the approval decision. Store all review decisions in Amazon DynamoD
C. Configure Amazon EventBridge rules with custom event patterns to route AI recommendations to human technicians for review. Create AWS Glue jobs to process human technician approval queues.Use Amazon ElastiCache to cache all human technician review decisions.
D. Create an AWS Glue workflow that has a human approval step. After the human technician review, integrate the application with an AWS Lambda function that calls the SendTaskSuccess API. Store all human technician review decisions in Amazon DynamoD

Answer: B

NEW QUESTION # 145
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?

A. Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.
B. Configure an AWS Lambda function to fetch routing configuration from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
C. Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.
D. Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.

Answer: B

NEW QUESTION # 146
......

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