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Amazon AIP-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Optimize and Operationalize a Generative AI Application30%- Implement monitoring, logging, and evaluation
  • 1. Monitor model performance and application metrics
  • 2. Implement human-in-the-loop evaluation workflows
  • 3. Evaluate model outputs for quality and bias
  • 4. Implement logging for prompts and responses
- 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. Manage model updates and rollback strategies
  • 4. Implement version control for prompts and models
- Optimize costs and performance
  • 1. Implement caching strategies for frequent queries
  • 2. Optimize model selection and parameter tuning
  • 3. Optimize token usage and manage costs
  • 4. Implement auto-scaling for AI workloads
Topic 2: Build and Implement a Generative AI Application45%- Develop the application using AWS AI services
  • 1. Implement prompt engineering and template management
  • 2. Implement inference calls to Amazon Bedrock or other FMs
  • 3. Implement retrieval-augmented generation (RAG) patterns
  • 4. Integrate with knowledge bases
- 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. Ensure compliance with AI ethics and responsible use
  • 2. Implement content filtering and safety mechanisms
  • 3. Implement IAM roles and policies for AI services
  • 4. Implement data encryption and privacy controls
Topic 3: Plan and Design a Generative AI Application25%- Select the appropriate foundation models (FMs) and techniques
  • 1. Consider fine-tuning vs. retrieval-augmented generation (RAG)
  • 2. Evaluate foundation models for the use case
  • 3. Design prompt engineering strategies
  • 4. Select model parameters and configurations
- 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

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

NEW QUESTION # 141
A company is developing a generative AI (GenAI)-powered customer support application that uses Amazon Bedrock foundation models (FMs). The application must maintain conversational context across multiple interactions with the same user. The application must run clarification workflows to handle ambiguous user queries. The company must store encrypted records of each user conversation to use for personalization. The application must be able to handle thousands of concurrent users while responding to each user quickly.
Which solution will meet these requirements?

Answer: A

Explanation:
Option B is the correct solution because it provides a scalable, durable, and secure architecture for conversational GenAI workloads that require multi-step clarification workflows and persistent memory.
AWS Step Functions Standard workflows are designed for long-running, stateful workflows with high reliability, which is ideal for clarification loops that may require multiple back-and-forth interactions. The Wait for a Callback pattern allows the workflow to pause while awaiting additional user input, making it well- suited for handling ambiguous queries without losing execution state.
Storing conversation history in Amazon DynamoDB enables millisecond-latency reads and writes at massive scale, supporting thousands of concurrent users. DynamoDB's on-demand capacity mode automatically scales with traffic, eliminating capacity planning. Server-side encryption ensures that stored conversation data is encrypted at rest, meeting security and compliance requirements for personalized data.
Option A uses Step Functions Express and Amazon RDS, which is not ideal for long-lived conversational workflows and introduces scaling and connection management challenges. Option C stores conversations as individual S3 objects, which increases latency and complicates context retrieval. Option D relies on Amazon ElastiCache, which is optimized for ephemeral caching rather than durable, auditable conversation history.
Therefore, Option B best balances scalability, performance, durability, and security for a conversational Amazon Bedrock-based customer support application.


NEW QUESTION # 142
A company uses an organization in AWS Organizations with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock across multiple accounts. The company must prevent specific topics and proprietary information from being included in prompts to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company wants to manage these controls centrally.
Which combination of solutions will meet these requirements? (Select TWO.)

Answer: C,D

Explanation:
The correct combination is C and D because together they enforce centralized governance over both model access and prompt content controls, which are the two core requirements of the scenario.
To ensure employees can use only approved Amazon Bedrock models, governance must be enforced at the organization level and not rely on individual application logic. Service Control Policies (SCPs) are the strongest control mechanism available in AWS Organizations because they define the maximum permissions an account or principal can have. In option C, the SCP prevents any Amazon Bedrock model invocation unless a centrally approved guardrail identifier is specified. This ensures that guardrails are always enforced, regardless of how or where the invocation originates. The additional use of IAM permissions boundaries ensures that even within allowed accounts, employees are restricted to invoking only explicitly approved foundation models.
To prevent specific topics and proprietary information from being included in prompts, Amazon Bedrock Guardrails must be used. Guardrails operate inline during model invocation and can block disallowed content before it is processed by the model. Option D correctly specifies a block filtering policy, which is appropriate when content must be prevented entirely rather than partially redacted. Deploying the guardrail using AWS CloudFormation StackSets allows the company to centrally manage and consistently deploy the same guardrail configuration across all accounts in the organization, ensuring uniform enforcement.
Option E uses mask filtering, which is better suited for redacting sensitive output rather than preventing prohibited content from being submitted in prompts. Option B attempts to use SCPs alone but does not enforce guardrail deployment or content filtering. Option A incorrectly places guardrail enforcement in permissions boundaries, which are not designed to validate request parameters such as guardrail identifiers.
By combining SCP-based enforcement with centrally deployed Bedrock guardrails, options C and D together provide strong, scalable, and centrally managed controls for both content safety and model governance across the organization.


NEW QUESTION # 143
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution.
The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units.
Which solution will meet these requirements?

Answer: A

Explanation:
Option A is the correct solution because it provides proactive, model-aware token management with fine- grained visibility and alerting, which is required for regulated financial workloads. Amazon Bedrock currently exposes token usage metrics after invocation, but it does not natively enforce proactive, model-specific token limits across multiple applications or business units.
By implementing model-specific tokenizers in AWS Lambda, the company can estimate input and output token usage before sending requests to Amazon Bedrock. This enables early detection of requests that are approaching or exceeding model limits and allows the application to block, truncate, or reroute requests proactively rather than reacting to failures.
Publishing token usage metrics to Amazon CloudWatch enables real-time monitoring and alerting at scale, easily supporting more than 5,000 requests per minute. Storing detailed token usage data in Amazon DynamoDB allows the company to attribute usage and costs to specific applications, teams, or business units-an essential requirement for regulatory reporting and internal chargeback.
Option B is incorrect because Amazon Bedrock Guardrails do not currently provide token quota enforcement or proactive token alerts. Option C is reactive and only analyzes failures after they occur. Option D throttles requests but cannot enforce token-based limits or provide per-model cost attribution.
Therefore, Option A best satisfies proactive alerting, scalability, compliance reporting, and cost allocation requirements with acceptable operational effort.


NEW QUESTION # 144
A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store I/O records. Which solution will meet these requirements?

Answer: C

Explanation:
AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for
" tamper-evident, immutable audit logs " is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.


NEW QUESTION # 145
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: B

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 # 146
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