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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

Topic 1: Optimize and Operationalize a Generative AI Application

30%

- 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 testing strategies for generative AI applications
4. Implement version control for prompts and models

- 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

- Optimize costs and performance

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

Topic 2: Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

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

- Implement data preprocessing and vectorization pipelines

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

- Implement security, compliance, and responsible AI

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

Topic 3: Plan and Design a Generative AI Application

25%

- Select the appropriate foundation models (FMs) and techniques

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

- 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. Design for scalability, reliability, and cost-effectiveness
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. Define functional and non-functional requirements
2. Identify constraints and risks
3. Identify the target audience and use cases
4. Determine data requirements and availability

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q150-Q155):

NEW QUESTION # 150
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios.
The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company ' s approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)

A. Add the high-risk conversation patterns to a denied topics guardrail.
B. Set a low grounding score threshold.
C. Configure a content filter guardrail to filter prompts that contain the high-risk conversation patterns.
D. Set a high grounding score threshold.
E. Configure a content filter guardrail to filter prompts that contain competitor names.
F. Add the names of competitors as custom word filters. Set the input and output actions to block.

Answer: A,D,F

NEW QUESTION # 151
A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?

A. Set up an S3 Lifecycle configuration to remove reports that are older than 3 years. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge base. When users interact with the AI assistant, apply a guardrail configuration selected based on the user's Cognito user group to redact PII from responses when required.
B. Create a second knowledge base. Use Lambda and Amazon Comprehend to redact PII before syncing to the second knowledge base. Route users to the appropriate knowledge base based on Cognito group membership.
C. Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploaded. Use a second Lambda function with Amazon Comprehend to redact PII for engineers. Use S3 Lifecycle rules to remove reports older than 3 years.
D. Enable Amazon Macie PII detection on the S3 bucket. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the reports. Configure the Lambda function to delete outdated documents and invoke knowledge base syncing.

Answer: A

Explanation:
Option C is the correct solution because it enforces privacy controls at inference time, not at ingestion time, which is required when different user roles require different visibility into the same underlying data.
Using an S3 Lifecycle configuration ensures that documents older than 3 years are automatically removed, guaranteeing that the knowledge base references only compliant, recent medical reports. Scheduling Lambda- based syncs keeps the knowledge base aligned with the bucket contents without introducing complex per- upload orchestration.
The most important requirement is role-based PII exposure. Amazon Bedrock guardrails support dynamic application at inference time, allowing the system to select a guardrail configuration based on the authenticated user's Amazon Cognito group. Surgeons can receive full responses, while engineers receive responses with PII masked-without duplicating data or maintaining multiple knowledge bases.
This approach preserves a single source of truth for medical reports while enforcing privacy through response- level controls. It also maintains full auditability of access and redaction behavior.
Option A permanently removes PII and violates surgeon access requirements. Option B redacts data inconsistently and couples privacy logic to ingestion. Option D doubles storage, increases cost, and introduces data drift risk.
Therefore, Option C best meets privacy, compliance, scalability, and operational efficiency requirements.

NEW QUESTION # 152
A multinational bank wants to implement a RAG solution on AWS to run queries on internal policy and compliance documents. To comply with data residency regulations, the company must ensure that critical customer data remains within a specific AWS Region. The bank wants to use foundation models (FMs) on AWS to reduce infrastructure costs and minimize model maintenance.
Which solution will meet these requirements?

A. Store critical customer data locally by using AWS Outposts in the regulated Region. Create embeddings locally in a secure vector store. Use Amazon Bedrock to orchestrate LLMs on AWS by using only retrieved context.
B. Store critical customer data in a dedicated Amazon S3 bucket in the regulated Region. Configure Amazon Bedrock to directly access the documents through a VPC endpoint. Perform all RAG retrieval on AWS.
C. Containerize FMs on Amazon ECS in the regulated Region. Ingest critical customer data into the containerized FMs. Perform RAG queries entirely inside the container.
D. Store critical customer data in a private Amazon OpenSearch Service cluster in a Region that is close to the regulated Region. Configure end-to-end encryption. Use an LLM on AWS to retrieve and summarize the data dynamically without storing embeddings locally.

Answer: B

Explanation:
Option A is the appropriate AWS-native approach because it keeps the authoritative documents in an Amazon S3 bucket located in the regulated Region while using managed Amazon Bedrock foundation-model infrastructure instead of maintaining containerized models. Amazon Bedrock supports private connectivity through AWS PrivateLink interface VPC endpoints. Applications inside the VPC can therefore reach Bedrock privately without requiring an internet gateway, NAT gateway, public IP address, or public internet path.
Amazon Bedrock Knowledge Bases can use Amazon S3 as a RAG data source. AWS requires the S3 bucket used by a knowledge base to be in the same Region as the Amazon Bedrock knowledge base. During ingestion, source content is converted into vector embeddings and stored in the configured vector store so similarity-based retrieval can locate relevant passages for augmentation. This creates an AWS-managed RAG architecture while preserving explicit regional placement of the underlying source documents.
Option B introduces AWS Outposts unnecessarily. More importantly, AWS documentation states that S3 on Outposts object data physically remains on the Outpost and "is not in an AWS Region." That conflicts with a requirement written specifically as keeping data within a particular AWS Region and also introduces additional infrastructure and operational cost.
C fails because "a Region close to" the regulated Region does not meet a requirement for the specified Region. Encryption does not override residency requirements. D requires the company to deploy and operate foundation-model containers, directly contradicting the objective of minimizing model infrastructure and maintenance.
Therefore, regional S3 storage combined with privately accessed managed Amazon Bedrock services provides the closest match to the stated residency, RAG, cost, and operational requirements.

NEW QUESTION # 153
A financial services company is deploying a GenAI assistant that uses Amazon Bedrock to answer customer questions about account activity and company policies. The company must comply with responsible AI principles and governance requirements.
The solution must meet the following requirements:
* Prevent harmful, biased, or non-compliant responses.
* Ensure consistent policy enforcement across all model invocations.
* Provide traceability and auditability for AI-generated outputs.
* Maintain developer productivity without embedding complex safety logic in application code.
Which solution will meet these requirements?

A. Log all prompts and responses to Amazon S3. Conduct periodic human reviews to identify violations of responsible AI principles.
B. Use Amazon Bedrock Guardrails to enforce content policies. Configure response filtering and topic restrictions. Configure logging for model interactions.
C. Use prompt engineering techniques and system prompts to guide the model ' s behavior.
D. Implement post-processing checks in AWS Lambda functions to manually review and redact model outputs before returning responses to users.

Answer: B

Explanation:
Amazon Bedrock Guardrails is the AWS-managed mechanism specifically intended to implement consistent generative-AI safeguards without forcing developers to reproduce moderation logic throughout application code. Guardrails supports configurable content filters for categories including hate, insults, sexual content, violence, misconduct, and prompt attacks. It also supports denied topics, custom word filters, and sensitive- information filters for PII and other patterns.
For a financial-services assistant, denied topics can prevent conversations outside approved policy boundaries, while content filtering can prevent harmful or inappropriate responses. Sensitive-information controls can detect and filter regulated or personally identifiable information. Because a guardrail configuration is applied as a managed policy during inference, the same safeguards can be applied consistently across invocations rather than depending on every developer to implement identical conditional logic correctly.
Logging model interactions adds the required traceability layer. Operational records of prompts, model invocations, blocked interactions, and responses can support investigations, compliance review, and responsible-AI governance. This is materially stronger than merely establishing behavioral expectations in prompt text.
B is insufficient because system prompts are instructions to the model rather than an independent enforcement mechanism. A model can still generate undesirable content or encounter adversarial inputs despite well- engineered prompts. C can implement controls but creates custom application logic that must be maintained, tested, and kept synchronized across every application path, which conflicts with the productivity requirement. D is entirely retrospective; periodic review can identify violations after they occur but does not prevent an unsafe response from reaching the customer.
The architectural principle is to separate deterministic governance controls from application prompting.
Guardrails supplies a reusable policy layer around foundation-model interactions, while centralized logging provides an auditable operational record. Consequently, A satisfies safety enforcement, consistency, governance, auditability, and developer-productivity requirements together.

NEW QUESTION # 154
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 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
C. 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
D. 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.

Answer: C

NEW QUESTION # 155
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