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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: Build and Implement a Generative AI Application

45%

- Implement data preprocessing and vectorization pipelines

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

- Implement security, compliance, and responsible AI

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

- Develop the application using AWS AI services

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

Topic 2: Plan and Design a Generative AI Application

25%

- Design the generative AI solution architecture

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

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

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

- Select the appropriate foundation models (FMs) and techniques

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

Topic 3: Optimize and Operationalize a Generative AI Application

30%

- Optimize costs and performance

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

- Implement monitoring, logging, and evaluation

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

- Implement CI/CD and automation for AI applications

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q83-Q88):

NEW QUESTION # 83
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?

A. Configure a cross-Region Amazon Bedrock guardrail to apply a set of content and word filters. Set the guardrail to detect mode during development and testing. Switch to mask mode for production deployment.
B. Configure an Amazon Bedrock guardrail to apply content and topic filters. Set the guardrail to detect mode during development, testing, and production. Disable invocation logging for the Amazon Bedrock model.
C. Configure an Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to mask mode during development and testing. Switch to block mode for production deployment. Deploy a copy of the guardrail to each Region where the company operates.
D. Configure a cross-Region Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to detect mode during development and testing. Switch to block mode for production deployment.

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 # 84
A media company is building an AI-powered content moderation system by using Amazon Bedrock. The system first classifies text by using a small, low-latency model. Then the system escalates requests that have a confidence score below 0.65 to a larger, more expensive model.
The system must respond in near real time for high-confidence results. The system must process low- confidence requests asynchronously. The system must scale to meet sudden spikes in demand. The company wants to optimize costs for the system by invoking the larger model only when required. The company wants to use decoupled components to achieve high resiliency for the system.
Which solution will meet these requirements?

A. Use Amazon API Gateway to invoke the small model synchronously. If the small model's confidence score is below 0.65, synchronously call the larger model. Use provisioned concurrency to handle traffic spikes.
B. Use an AWS Step Functions workflow that has parallel branches to run both the small model and the large model for every request. Choose the large model result when confidence score values differ.
C. Send requests to an Amazon SQS queue. Use AWS Fargate to process messages. Invoke the small model first. If the confidence score is below 0.65, place the request in a second SQS queue to process asynchronously by using the large model.
D. Deploy both models on Amazon EC2 instances and enable auto scaling. Use a custom application heuristic to route requests to the appropriate instance based on phrase length and keyword rules.

Answer: C

Explanation:
Option C is the best answer because it implements a decoupled, queue-based moderation pipeline that invokes the expensive model only when the low-latency model is not confident enough. Amazon SQS is designed to decouple distributed application components and support asynchronous processing. AWS documentation describes SQS as a fully managed message queuing service that enables decoupling and scaling of microservices, distributed systems, and serverless applications. This matches the requirement for high resiliency and sudden demand spikes because incoming requests can be buffered in a durable queue rather than overwhelming the model-processing layer.
Using AWS Fargate to process queue messages is also appropriate because Fargate provides serverless container compute for Amazon ECS or Amazon EKS workloads. It allows the company to run scalable processing workers without managing EC2 capacity directly. AWS Prescriptive Guidance includes architectures that use API Gateway, Amazon SQS, and AWS Fargate to process events asynchronously, which supports the same decoupled processing model required in this question.
The two-stage queue design also optimizes cost. The small, low-latency classifier is used first for all requests.
Only requests with confidence below 0.65 are placed into the second queue and processed by the larger model. This avoids running the larger model for every moderation request. High-confidence results can be completed quickly by the first-stage processor, while uncertain results are isolated into an asynchronous second-stage workflow.
Option A is incorrect because it synchronously calls the larger model for low-confidence results, which violates the requirement to process low-confidence requests asynchronously. Option B is incorrect because it invokes both models for every request, increasing cost and eliminating the benefit of confidence-based escalation. Option D requires managing EC2 instances and uses keyword heuristics instead of model confidence, so it is less resilient and less aligned with Bedrock-based moderation. Therefore, option C is correct.

NEW QUESTION # 85
A company is developing a generative AI (GenAI) application that analyzes customer service calls in real time and generates suggested responses for human customer service agents. The application must process
500,000 concurrent calls during peak hours with less than 200 ms end-to-end latency for each suggestion. The company uses existing architecture to transcribe customer call audio streams. The application must not exceed a predefined monthly compute budget and must maintain auto scaling capabilities.
Which solution will meet these requirements?

A. Deploy a low-latency, real-time optimized model on Amazon Bedrock. Purchase provisioned throughput and set up automatic scaling policies.
B. Deploy a mid-sized language model on an Amazon SageMaker serverless endpoint that is optimized for batch processing.
C. Deploy a large, complex reasoning model on Amazon Bedrock. Purchase provisioned throughput and optimize for batch processing.
D. Deploy a large language model (LLM) on an Amazon SageMaker real-time endpoint that uses dedicated GPU instances.

Answer: A

Explanation:
Option B is the correct solution because it aligns with AWS guidance for building high-throughput, ultra-low- latency GenAI applications while maintaining predictable costs and automatic scaling. Amazon Bedrock provides access to foundation models that are specifically optimized for real-time inference use cases, including conversational and recommendation-style workloads that require responses within milliseconds.
Low-latency models in Amazon Bedrock are designed to handle very high request rates with minimal per- request overhead. Purchasing provisioned throughput ensures that sufficient model capacity is reserved to handle peak loads, eliminating cold starts and reducing request queuing during traffic surges. This is critical when supporting up to 500,000 concurrent calls with strict latency requirements.
Automatic scaling policies allow the application to dynamically adjust capacity based on demand, ensuring cost efficiency during off-peak hours while maintaining performance during peak usage. This directly supports the requirement to stay within a predefined monthly compute budget.
Option A fails because batch processing and complex reasoning models introduce higher latency and are not suitable for real-time suggestions. Option C introduces significantly higher operational and cost overhead due to dedicated GPU instances and manual scaling responsibilities. Option D is optimized for batch workloads and cannot meet the sub-200 ms latency requirement.
Therefore, Option B provides the best balance of performance, scalability, cost control, and operational simplicity using AWS-native GenAI services.

NEW QUESTION # 86
A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM's responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.
Which solution will meet these requirements?

A. Use Amazon OpenSearch Service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.
B. Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.
C. Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.
D. Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan Embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.

Answer: A

Explanation:
Option B is the correct solution because legal research workloads require both semantic understanding and exact lexical precision, especially for statutes, citations, and domain-specific terminology. A hybrid search architecture directly addresses this need by combining vector similarity search with traditional keyword-based retrieval.
Vector search alone is often insufficient for legal research because exact phrases, citation formats, and jurisdiction-specific terms must be matched precisely. Keyword search ensures high recall and precision for citations and legal terms, while vector search captures deeper semantic relationships between legal concepts, precedents, and arguments. Amazon OpenSearch Service natively supports hybrid search, enabling efficient scoring and ranking without external orchestration.
Applying an Amazon Bedrock reranker model further improves relevance by reordering retrieved documents based on deeper contextual understanding. Reranking is especially valuable in legal research because multiple documents may appear relevant, but only a subset truly addresses the user's legal question. The reranker optimizes final results before they are passed to the Anthropic Claude FM, improving answer accuracy and reducing hallucinations.
Option A relies on default vector search, which does not reliably handle citations and exact terminology.
Option C focuses on query suggestions and post-processing rather than retrieval quality. Option D introduces unnecessary operational complexity by merging results across multiple systems.
Therefore, Option B best meets the requirements for precision, performance, and semantic understanding in a legal research AI assistant.

NEW QUESTION # 87
A company is designing a solution that uses foundation models (FMs) to support multiple AI workloads.
Some FMs must be invoked on demand and in real time. Other FMs require consistent high-throughput access for batch processing.
The solution must support hybrid deployment patterns and run workloads across cloud infrastructure and on- premises infrastructure to comply with data residency and compliance requirements.
Which combination of steps will meet these requirements? (Select TWO.)

A. Deploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using Amazon SageMaker Neo. Orchestrate the FMs by using AWS Lambda to support hybrid deployment.
B. Configure provisioned throughput in Amazon Bedrock to ensure consistent performance for high- volume workloads.
C. Use Amazon Bedrock with auto-scaling to handle unpredictable traffic surges.
D. Use Amazon SageMaker JumpStart to host and invoke the FMs.
E. Use AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon SageMaker AI asynchronous endpoints.

Answer: A,B

Explanation:
The correct combination is B and C because together they address both workload diversity and hybrid deployment requirements with minimal custom engineering.
Option B provides consistent, high-throughput access by configuring provisioned throughput in Amazon Bedrock. Provisioned throughput guarantees predictable capacity and performance, which is essential for batch processing workloads that require sustained inference rates. This eliminates cold starts and throttling concerns that can occur with purely on-demand usage, making it well suited for high-volume enterprise workloads.
Option C enables hybrid deployment across cloud and on-premises environments by deploying foundation models to Amazon SageMaker AI endpoints and using Amazon SageMaker Neo for edge and on-premises optimization. SageMaker Neo compiles models for target hardware, allowing inference to run efficiently outside the AWS cloud while still using AWS-managed tooling. Orchestrating these deployments with AWS Lambda allows consistent invocation patterns across environments.
Option A uses asynchronous endpoints, which are not suitable for real-time, low-latency inference. Option D addresses scaling but does not support on-premises or hybrid deployment. Option E simplifies model onboarding but does not address hybrid execution or guaranteed throughput.
Therefore, Options B and C together provide real-time and batch support, predictable performance, and true hybrid deployment while minimizing operational overhead.

NEW QUESTION # 88
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