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Amazon AIP-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Optimize and Operationalize a Generative AI Application30%- Implement CI/CD and automation for AI applications
  • 1. Implement version control for prompts and models
  • 2. Automate deployment of AI models and applications
  • 3. Implement testing strategies for generative AI applications
  • 4. Manage model updates and rollback strategies
- Optimize costs and performance
  • 1. Implement auto-scaling for AI workloads
  • 2. Optimize token usage and manage costs
  • 3. Optimize model selection and parameter tuning
  • 4. Implement caching strategies for frequent queries
- Implement monitoring, logging, and evaluation
  • 1. Implement human-in-the-loop evaluation workflows
  • 2. Evaluate model outputs for quality and bias
  • 3. Implement logging for prompts and responses
  • 4. Monitor model performance and application metrics
Topic 2: Build and Implement a Generative AI Application45%- Implement data preprocessing and vectorization pipelines
  • 1. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora)
  • 2. Implement document chunking and text processing
  • 3. Implement embeddings generation using AWS services
  • 4. Ingest and transform data for model consumption
- Implement security, compliance, and responsible AI
  • 1. Implement content filtering and safety mechanisms
  • 2. Implement IAM roles and policies for AI services
  • 3. Implement data encryption and privacy controls
  • 4. Ensure compliance with AI ethics and responsible use
- Develop the application using AWS AI services
  • 1. Implement retrieval-augmented generation (RAG) patterns
  • 2. Integrate with knowledge bases
  • 3. Implement inference calls to Amazon Bedrock or other FMs
  • 4. Implement prompt engineering and template management
Topic 3: Plan and Design a Generative AI Application25%- 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. Determine data requirements and availability
  • 4. Identify constraints and risks
- Select the appropriate foundation models (FMs) and techniques
  • 1. Select model parameters and configurations
  • 2. Consider fine-tuning vs. retrieval-augmented generation (RAG)
  • 3. Design prompt engineering strategies
  • 4. Evaluate foundation models for the use case
- Design the generative AI solution architecture
  • 1. Design for scalability, reliability, and cost-effectiveness
  • 2. Design the end-to-end solution architecture
  • 3. Integrate with other AWS services (e.g., storage, databases, security)
  • 4. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker)

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
A healthcare company is developing a document management system that stores medical research papers in an Amazon S3 bucket. The company needs a comprehensive metadata framework to improve search precision for a GenAI application. The metadata must include document timestamps, author information, and research domain classifications.
The solution must maintain a consistent metadata structure across all uploaded documents and allow foundation models (FMs) to understand document context without accessing full content.
Which solution will meet these requirements?

Answer: D

Explanation:
Option A is the correct solution because it uses native Amazon S3 metadata mechanisms to create a consistent, queryable, and model-friendly metadata framework with minimal complexity. S3 system metadata automatically records object creation and modification timestamps, providing reliable and consistent temporal context without additional processing.
Custom user-defined metadata is the appropriate mechanism for storing structured attributes such as author information. These key-value pairs are stored directly with the object, remain consistent across uploads, and can be accessed programmatically by downstream indexing or retrieval systems used by GenAI applications.
S3 object tags are ideal for domain classification because they are designed for lightweight categorization, filtering, and access control. Tags can be standardized across the organization to ensure consistent research domain labeling and can be consumed by search indexes or knowledge base ingestion pipelines without requiring access to the full document body.
Together, system metadata, user-defined metadata, and object tags provide a clean separation of concerns:
timestamps for temporal context, metadata for authorship, and tags for classification. This structure allows foundation models to reason about document context (such as recency, domain relevance, and authorship) based on metadata alone, improving retrieval precision and reducing unnecessary token usage.
Options B, C, and D misuse features like Object Lock, access points, Storage Lens, or event notifications for purposes they were not designed for, adding complexity without improving metadata quality or model understanding.
Therefore, Option A best satisfies the metadata consistency, context enrichment, and low-overhead requirements for GenAI-driven document analysis.


NEW QUESTION # 14
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?

Answer: B

Explanation:
Option C best satisfies the requirement to change routing decisions without redeploying code while supporting complex, frequently changing business logic at scale. AWS AppConfig is designed for centrally managing dynamic configuration (feature flags, rules, thresholds, and policy parameters) and deploying changes safely. It supports controlled deployments, validation, and rapid propagation of updated configuration values, which aligns with "real-time cost metrics that change hourly" and the need for "immediate propagation across thousands of concurrent requests." In this design, the Lambda function becomes the policy decision point. For each request, it evaluates user attributes (tier, transaction value), context (regulatory zone, Region), and live cost/performance thresholds stored in AppConfig to determine which Amazon Bedrock FM to invoke. Because the routing rules and FM identifiers are delivered as configuration, the company can switch models, adjust A/B testing weights, or update compliance routing rules by deploying new AppConfig configuration versions rather than pushing new application code. This reduces operational risk and accelerates iteration.
Exposing a single API Gateway endpoint also minimizes client complexity and keeps routing logic server- side, which is important when rules change frequently. Lambda can cache configuration between invocations (within the execution environment) to reduce repeated fetch overhead while still picking up changes quickly, enabling both low latency and rapid rule rollout under high concurrency.
Option A relies on Lambda environment variables, which are not intended for frequent real-time updates and typically require function configuration updates that are slower and operationally brittle. Option B uses mapping templates and stage variables, which are limited for complex rule evaluation and safe rollout patterns. Option D misuses authorizers for business routing, adds extra latency and complexity, and complicates observability and error handling by splitting decisioning from execution.


NEW QUESTION # 15
A global research company is building a RAG-enabled AI system that uses Amazon Bedrock Knowledge Bases. The company stores documents in Amazon S3 and indexes the documents into an Amazon OpenSearch Serverless vector collection.
When the company evaluates the system, the company identifies three issues. Queries return outdated documents when users request only recent research. Medical research queries return both medical research documents and engineering domain documents. Users can retrieve documents that were authored by researchers who the users should not have access to based on company policies.
The company wants to improve the system so that retrieval becomes more precise and contextually appropriate.
Which solution will meet these requirements?

Answer: B

Explanation:
Option B is correct because the retrieval problem requires filtering before semantic similarity search, not merely returning metadata after retrieval. Amazon Bedrock Knowledge Bases can apply filters to metadata associated with source documents, so timestamp, author, and research-domain attributes can restrict the candidate set before vector results are returned. This prevents old, wrong-domain, or unauthorized-author documents from entering the retrieved context. AWS also documents S3 knowledge-base metadata files and filterable metadata attributes. Option A only returns metadata with already-selected vector results, so irrelevant documents can still be retrieved. Option C introduces DynamoDB and a two-stage custom retrieval path. Option D adds Comprehend and Aurora, increasing operational complexity. The key design principle is to make the required attributes filterable in the vector retrieval layer. AWS Documentation


NEW QUESTION # 16
A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.
Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 17
A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.
Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.
Which solution will meet these requirements?

Answer: D

Explanation:
Option A best matches the requirement because managed knowledge-base retrieval provides source attribution and lets access be governed through IAM, avoiding custom result-merging logic. Amazon Bedrock RetrieveAndGenerate returns citations tied to retrieved source references. One implementation detail matters under current AWS documentation: structured Knowledge Bases use Amazon Redshift as the query engine; Amazon Redshift federated queries can access Amazon RDS and Aurora operational databases. Therefore, the RDS-backed knowledge-base leg should be implemented through that supported structured-data path rather than interpreted as a direct native RDS vector connector. Options B, C, and D introduce extra agents or Lambda-based retrieval/merging, which increases development effort and makes unified citation handling more custom. A keeps both information sources behind managed retrieval and preserves provenance while IAM controls access. AWS Documentation


NEW QUESTION # 18
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