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Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	AI Safety, Security, and Governance: This domain addresses input output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.
Topic 2	Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.
Topic 3	Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.
Topic 4	Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
Topic 5	Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q53-Q58):

NEW QUESTION # 53
A company wants to select a new FM for its AI assistant. A GenAI developer needs to generate evaluation reports to help a data scientist assess the quality and safety of various foundation models FMs. The data scientist provides the GenAI developer with sample prompts for evaluation. The GenAI developer wants to use Amazon Bedrock to automate report generation and evaluation.
Which solution will meet this requirement?

A. Combine the sample prompts into a single JSON document. Create an Amazon Bedrock knowledge base with the document. Write a prompt that asks the FM to generate a response to each sample prompt.
Use the RetrieveAndGenerate API to generate a report for each model.
B. Combine the sample prompts into a single JSON document. Create an Amazon Bedrock knowledge base from the document. Create an Amazon Bedrock evaluation job that uses the retrieval and response generation evaluation type. Specify an Amazon S3 bucket as the output. Run an evaluation job for each FM.
C. Combine the sample prompts into a single JSONL document. Store the document in an Amazon S3 bucket. Create an Amazon Bedrock evaluation job that uses a judge model. Specify the S3 location as input and Amazon QuickSight as output. Run an evaluation job for each FM and select the FM as the evaluator.
D. Combine the sample prompts into a single JSONL document. Store the document in an Amazon S3 bucket. Create an Amazon Bedrock evaluation job that uses a judge model. Specify the S3 location as input and a different S3 location as output. Run an evaluation job for each FM and select the FM as the generator.

Answer: D

Explanation:
Option B is correct because it uses the managed evaluation capability in Amazon Bedrock that is intended specifically for comparing foundation models using a consistent prompt set and producing structured results with minimal custom tooling. In a Bedrock evaluation workflow, you provide an input dataset of prompts, typically in JSON Lines format so each line represents one evaluation record. Storing the JSONL file in Amazon S3 allows Bedrock to read the dataset at scale and write standardized evaluation outputs back to S3 for downstream analysis, sharing, and retention.
The key requirement is to assess both quality and safety across multiple models. A Bedrock evaluation job can use a judge model to score the generated outputs against defined criteria. This approach supports repeatable, apples-to-apples comparisons because the same judge model and scoring rubric can be applied to every candidate foundation model. The candidate models are configured as generators, meaning each evaluation job run uses one selected FM to produce answers for the same prompt set, and the judge model evaluates those answers. That matches the requirement to generate evaluation reports that help a data scientist select the best FM.
Option A does not use Bedrock evaluation jobs, and a knowledge base plus RetrieveAndGenerate is a RAG pattern, not an evaluation framework. It would produce responses but not standardized scoring and reporting suitable for model selection. Option C is incorrect because Bedrock evaluation outputs are delivered to S3, not directly to a BI destination, and selecting the candidate FM as the evaluator conflicts with the intended pattern of using a stable judge model. Option D misuses knowledge bases and retrieval evaluation types when the requirement is prompt-based model assessment rather than evaluating retrieval quality.

NEW QUESTION # 54
A company uses an AI assistant application to summarize the company's website content and provide information to customers. The company plans to use Amazon Bedrock to give the application access to a foundation model (FM).
The company needs to deploy the AI assistant application to a development environment and a production environment. The solution must integrate the environments with the FM. The company wants to test the effectiveness of various FMs in each environment. The solution must provide product owners with the ability to easily switch between FMs for testing purposes in each environment.
Which solution will meet these requirements?

A. Create one AWS CDK application for the production environment. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.ProvisionedModel.fromProvisionedModelArn() method. Create a pipeline in AWS CodePipeline. Configure the pipeline to deploy to the production environment by using an AWS CodeBuild deploy action. For the development environment, manually recreate the resources by referring to the production application code.
B. Create one AWS CDK application. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.FoundationModel.fromFoundationModelId() method. Create a pipeline in AWS CodePipeline that has a deployment stage for each environment that uses AWS CodeBuild deploy actions.
C. Create one AWS CDK application. Create multiple pipelines in AWS CodePipeline. Configure each pipeline to have its own settings for each FM. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.ProvisionedModel.fromProvisionedModelArn() method.
D. Create a separate AWS CDK application for each environment. Configure the applications to invoke the Amazon Bedrock FMs by using the aws_bedrock.FoundationModel.fromFoundationModelId() method. Create a separate pipeline in AWS CodePipeline for each environment.

Answer: B

Explanation:
Option C best satisfies the requirement for flexible FM testing across environments while minimizing operational complexity and aligning with AWS-recommended deployment practices. Amazon Bedrock supports invoking on-demand foundation models through the FoundationModel abstraction, which allows applications to dynamically reference different models without requiring dedicated provisioned capacity. This is ideal for experimentation and A/B testing in both development and production environments.
Using a single AWS CDK application ensures infrastructure consistency and reduces duplication.
Environment-specific configuration, such as selecting different foundation model IDs, can be externalized through parameters, context variables, or environment-specific configuration files. This allows product owners to easily switch between FMs in each environment without modifying application logic.
A single AWS CodePipeline with distinct deployment stages for development and production is an AWS best practice for multi-environment deployments. It enforces consistent build and deployment steps while still allowing environment-level customization. AWS CodeBuild deploy actions enable automated, repeatable deployments, reducing manual errors and improving governance.
Option A increases complexity by introducing multiple pipelines and relies on provisioned models, which are not necessary for FM evaluation and experimentation. Provisioned throughput is better suited for predictable, high-volume production workloads rather than frequent model switching.
Option B creates unnecessary operational overhead by duplicating CDK applications and pipelines, making long-term maintenance more difficult.
Option D directly conflicts with infrastructure-as-code best practices by manually recreating development resources, which increases configuration drift and reduces reliability.
Therefore, Option C provides the most flexible, scalable, and AWS-aligned solution for testing and switching foundation models across development and production environments.

NEW QUESTION # 55
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. 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.
B. 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.
C. 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.
D. 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.

Answer: A

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 # 56
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?

A. Associate one Amazon Bedrock AgentCore agent with two knowledge bases. Configure one knowledge base to access documents from Amazon S3 and the other to access structured data from Amazon RDS.
Configure the agent to preserve attribution metadata in responses. Use IAM roles to control access to sensitive data.
B. Configure one Amazon Bedrock AgentCore agent to use a knowledge base that contains the documents from Amazon S3. Configure AWS Lambda functions to query Amazon RDS directly for structured data and to combine the results before returning responses.
C. Create two Amazon Bedrock AgentCore agents. Associate one agent with a knowledge base that is connected to the documents in Amazon S3 and the other agent with a knowledge base that is connected to Amazon RDS. Develop a custom AWS Lambda function to query both agents simultaneously and combine the results.
D. Create one Amazon Bedrock AgentCore agent that uses AWS Lambda functions to directly query Amazon S3 for documents and Amazon RDS for structured data. Process the responses and merge the results into a single response.

Answer: A

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 # 57
A financial services company wants to use Amazon Bedrock foundation models (FMs) to analyze call center recordings. When calls end, the call center stores recordings as MP3 files in an Amazon S3 bucket. The company needs to generate summaries and sentiment analysis for the recordings in a structured format as soon as new files are created. The recordings average 20 MB in size. Which combination of solutions will meet these requirements? (Select TWO.)

A. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to directly invoke Amazon Bedrock FMs to generate summaries and sentiment analysis in JSON format.
B. Configure the source S3 bucket to send events to Amazon EventBridge. Create an EventBridge rule to invoke the Step Functions workflow when an object is created in the bucket.
C. Configure the source S3 bucket to send notifications to the Step Functions workflow when an object is created in the bucket.
D. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to create a prompt to invoke Amazon Bedrock FMs to generate structured analysis output.
E. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to process the text by using Amazon Bedrock FMs to generate structured analysis output.

Answer: A,B

Explanation:
AWS Step Functions provides native service integrations that minimize code and operational overhead. For this workflow, Step Functions can directly invoke Amazon Transcribe and, upon completion, directly invoke Amazon Bedrock foundation models. Modern foundation models can be prompted to return outputs in JSON format , fulfilling the requirement for structured analysis without needing an intermediate Lambda function to format the request or response. To trigger the process " as soon as new files are created, " configuring Amazon S3 to send events to Amazon EventBridge is the recommended event-driven pattern. This allows for fine-grained routing and decouples the storage layer from the processing logic, ensuring the workflow scales reliably as call volume grows.

NEW QUESTION # 58
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