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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

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

Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

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

- Implement data preprocessing and vectorization pipelines

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

- Implement security, compliance, and responsible AI

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

Optimize and Operationalize a Generative AI Application

30%

- Optimize costs and performance

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

- Implement monitoring, logging, and evaluation

1. Monitor model performance and application metrics
2. Implement logging for prompts and responses
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. 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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q57-Q62):

NEW QUESTION # 57
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?

A. Create an identity provider (IdP) connection in IAM to authenticate by using Microsoft Entra ID.
Assign department permission sets to control access to specific model families. Deploy AWS Lambda functions in private subnets with a NAT gateway for egress to Amazon Bedrock public endpoints.
Enable CloudWatch Logs to capture model interactions for auditing purposes.
B. Configure OpenID Connect (OIDC) federation between Microsoft Entra ID and IAM. Use attribute- based access control to map department attributes to specific model access permissions. Apply SCP policies to restrict access to Amazon Bedrock FM families based on department. Use Microsoft Entra ID's built-in logging capabilities to maintain an audit trail of model interactions.
C. Create a SAML identity provider (IdP) in IAM to authenticate by using Microsoft Entra ID. Use IAM permissions boundaries to limit department roles' access to specific model families. Configure public Amazon Bedrock API endpoints with VPC routing to maintain private network connectivity. Set up CloudTrail with Amazon S3 Lifecycle rules to manage audit logs of model interactions.
D. Configure SAML federation between Microsoft Entra ID and AWS Identity and Access Management.
Create department-specific IAM roles that allow only the required ModelId values. Create AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services. Enable AWS CloudTrail to capture Amazon Bedrock API calls. Configure Amazon Bedrock model invocation logging to record detailed model interactions.

Answer: D

Explanation:
Option A is the correct solution because it satisfies authentication, private connectivity, fine-grained authorization, and auditing using AWS-recommended patterns.
SAML federation between Microsoft Entra ID and IAM is a mature, well-supported integration that enables centralized enterprise authentication. Department-specific IAM roles allow precise control over which Bedrock ModelId values each department can invoke, enforcing access by model family.
Using AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services ensures that all inference traffic stays on private AWS network paths, with no public internet exposure. NAT gateways and public endpoints, as used in other options, violate this requirement.
AWS CloudTrail provides authoritative audit logs of all Bedrock API calls, which is required for compliance.
Amazon Bedrock model invocation logging complements CloudTrail by capturing detailed prompt and response metadata for deeper auditing and investigation.
Option B uses public endpoints via NAT. Option C incorrectly claims public endpoints can be private. Option D relies on IdP-side logs, which do not capture Bedrock API activity.
Therefore, Option A is the only solution that fully meets security, compliance, and observability requirements.

NEW QUESTION # 58
A hotel company wants to enhance a legacy Java-based property management system (PMS) by adding AI capabilities. The company wants to use Amazon Bedrock Knowledge Bases to provide staff with room availability information and hotel-specific details. The solution must maintain separate access controls for each hotel that the company manages. The solution must provide room availability information in near real time and must maintain consistent performance during peak usage periods.
Which solution will meet these requirements?

A. Create an Amazon EventBridge rule for each hotel that is invoked by changes to the PMS database.
Configure the rule to send updates to a centralized Amazon Bedrock knowledge base in a management AWS account. Configure resource-based policies to enforce hotel-specific access controls.
B. Build a centralized Amazon Bedrock Agents solution that uses multiple knowledge bases. Implement AWS IAM Identity Center with hotel-specific permission sets to control staff access.
C. Deploy a single Amazon Bedrock knowledge base that contains combined data for all hotels. Configure AWS Lambda functions to synchronize data from each hotel's PMS database through direct API connections. Implement AWS CloudTrail logging with hotel-specific filters to audit access logs for each hotel's data.
D. Implement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide near real-time room availability information. Schedule regular synchronization for less critical information.

Answer: D

Explanation:
Option C best meets the requirements by aligning with AWS best practices for data isolation, access control, and scalable GenAI retrieval. Implementing a separate Amazon Bedrock knowledge base for each hotel ensures strict separation of data and permissions. This approach naturally enforces hotel-level access control without requiring complex policy logic or post-query filtering.
A multi-account structure further strengthens security and governance by isolating each hotel's data plane.
AWS recommends account-level isolation for workloads with strong tenancy or compliance boundaries. Hotel staff can be granted access only to their hotel's account and corresponding knowledge base, eliminating the risk of cross-hotel data exposure.
Direct data ingestion into each knowledge base enables near real-time updates for critical data such as room availability. For information that does not change frequently, scheduled synchronization reduces ingestion cost while maintaining accuracy. This hybrid ingestion model balances freshness and operational efficiency.
Because Amazon Bedrock Knowledge Bases are fully managed, performance remains consistent during peak usage periods without the company managing indexing, scaling, or retrieval infrastructure. Each knowledge base scales independently, preventing noisy-neighbor issues that could arise in a centralized design.
Option A and B rely on a centralized knowledge base, which increases policy complexity and introduces risk of misconfigured access controls. Option D adds unnecessary orchestration complexity and does not inherently solve real-time data freshness requirements.
Therefore, Option C provides the most secure, scalable, and operationally efficient solution for enhancing the PMS with Amazon Bedrock Knowledge Bases.

NEW QUESTION # 59
A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in PostgreSQL.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?

A. Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, features, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
B. Migrate restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline.Configure the knowledge base to automatically generate embeddings from restaurant information. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.
C. Keep the restaurant data in PostgreSQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.
D. Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM.
Perform k-nearest neighbors (k-NN) searches to find semantically similar results.

Answer: D

Explanation:
Option B best satisfies the requirements while minimizing development effort by combining managed semantic search capabilities with fully managed foundation models. AWS Generative AI guidance describes semantic search as a vector-based retrieval pattern where both documents and user queries are embedded into a shared vector space. Similarity search (such as k-nearest neighbors) then retrieves results based on meaning rather than exact keywords.
Amazon OpenSearch Service natively supports vector indexing and k-NN search at scale. This makes it well suited for large datasets such as 20 million restaurants and 200 million reviews while still achieving sub- second latency for the majority of queries. Because OpenSearch is a distributed, managed service, it automatically scales during peak traffic periods and provides cost-effective performance compared with building and tuning custom vector search pipelines on relational databases.
Using Amazon Bedrock to generate embeddings significantly reduces development complexity. AWS manages the foundation models, eliminates the need for custom model hosting, and ensures consistency by using the same FM for both document embeddings and query embeddings. This aligns directly with AWS- recommended semantic search architectures and removes the need for model lifecycle management.
Hourly updates to restaurant data can be handled efficiently through incremental re-indexing in OpenSearch without disrupting query performance. This approach cleanly separates transactional data storage from search workloads, which is a best practice in AWS architectures.
Option A does not meet the semantic search requirement because keyword-based search cannot reliably interpret complex natural language intent. Option C introduces scalability and performance risks by running large-scale vector similarity searches inside PostgreSQL, which increases operational complexity. Option D adds unnecessary ingestion and abstraction layers intended for retrieval-augmented generation, not high- throughput semantic search.
Therefore, Option B provides the optimal balance of performance, scalability, data freshness, and minimal development effort using AWS Generative AI services.

NEW QUESTION # 60
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?

A. Create an identity provider (IdP) connection in IAM to authenticate by using Microsoft Entra ID.
Assign department permission sets to control access to specific model families. Deploy AWS Lambda functions in private subnets with a NAT gateway for egress to Amazon Bedrock public endpoints.
Enable CloudWatch Logs to capture model interactions for auditing purposes.
B. Configure OpenID Connect (OIDC) federation between Microsoft Entra ID and IAM. Use attribute- based access control to map department attributes to specific model access permissions. Apply SCP policies to restrict access to Amazon Bedrock FM families based on department. Use Microsoft Entra ID's built-in logging capabilities to maintain an audit trail of model interactions.
C. Create a SAML identity provider (IdP) in IAM to authenticate by using Microsoft Entra ID. Use IAM permissions boundaries to limit department roles' access to specific model families. Configure public Amazon Bedrock API endpoints with VPC routing to maintain private network connectivity. Set up CloudTrail with Amazon S3 Lifecycle rules to manage audit logs of model interactions.
D. Configure SAML federation between Microsoft Entra ID and AWS Identity and Access Management.
Create department-specific IAM roles that allow only the required ModelId values. Create AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services. Enable AWS CloudTrail to capture Amazon Bedrock API calls. Configure Amazon Bedrock model invocation logging to record detailed model interactions.

Answer: D

NEW QUESTION # 61
A company is planning to deploy multiple generative AI (GenAI) applications to five independent business units that operate in multiple countries in Europe and the Americas. Each application uses Amazon Bedrock Retrieval Augmented Generation (RAG) patterns with business unit-specific knowledge bases that store terabytes of unstructured data.
The company must establish well-architected, standardized components for security controls, observability practices, and deployment patterns across all the GenAI applications. The components must be reusable, versioned, and governed consistently.
Which solution will meet these requirements?

A. Configure Amazon API Gateway REST API endpoints for the GenAI applications. Deploy common security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens in standardized AWS CloudFormation templates. Use CloudFormation Guard after deployment to validate policy compliance in each business unit.
B. Create standardized AWS CloudFormation templates to implement security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Establish a centralized repository for version control. Integrate a CI/CD pipeline with CloudFormation Guard to enforce consistent and repeatable deployments across business units.
C. Document security controls, observability requirements, and RAG patterns based on the AWS Well- Architected Generative AI Lens in a shared design document. Use Amazon Macie to enforce deployment. Delegate implementation responsibility to each business unit.
D. Use AWS Service Catalog to define standardized portfolios and versioned products for each business unit. Use the portfolios to enforce security, observability, and RAG patterns based on the AWS Well- Architected Generative AI Lens. Require business units to use the Service Catalog console to deploy resources.

Answer: B

Explanation:
Option B best meets the requirement for reusable, versioned, and consistently governed components across multiple business units because it implements "platform-level standardization" through infrastructure as code plus automated compliance enforcement before deployment. Standardized CloudFormation templates provide reusable building blocks for security controls (identity, networking boundaries, encryption), observability practices (metrics, logs, traces), and RAG deployment patterns (knowledge base integration, ingestion pipelines, retrieval controls). This aligns with AWS guidance to operationalize well-architected patterns through repeatable templates rather than ad hoc implementations.
A centralized repository enables version control, change review, and governance of templates across all five business units. This satisfies the "versioned" and "reusable" requirements and provides a single source of truth for approved architectures. Integrating a CI/CD pipeline ensures that deployments are consistent and automated, reducing drift between business units and Regions.
CloudFormation Guard is most effective when used as a preventive control in the pipeline, not only after deployment. By running Guard rules during build or pre-deploy stages, the organization can enforce mandatory security and observability configurations and block noncompliant changes before they reach production. This supports consistent governance while still enabling business units to deploy quickly.
Option A performs compliance validation after deployment, which allows policy violations to be deployed first and remediated later. Option C provides governed provisioning but requiring console-based deployment reduces automation and can slow standardized CI/CD adoption; it also adds an additional governance layer that is not required to meet the stated needs. Option D is not enforceable and does not provide reusable, versioned, governed components.
Therefore, Option B provides the strongest, most scalable, and most consistently governed approach for standardized GenAI deployments across business units.

NEW QUESTION # 62
......

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