AIP-C01 - AWS Certified Generative AI Developer - Professional–Professional Exam Sample Questions

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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Plan and Design a Generative AI Application	25%	- Design the generative AI solution architecture 1. Design the end-to-end solution architecture 2. Design for scalability, reliability, and cost-effectiveness 3. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker) 4. Integrate with other AWS services (e.g., storage, databases, security) - Select the appropriate foundation models (FMs) and techniques 1. Select model parameters and configurations 2. Evaluate foundation models for the use case 3. Design prompt engineering strategies 4. Consider fine-tuning vs. retrieval-augmented generation (RAG) - 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
Topic 2: Optimize and Operationalize a Generative AI Application	30%	- 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. Implement caching strategies for frequent queries 4. Optimize model selection and parameter tuning - Implement monitoring, logging, and evaluation 1. Implement human-in-the-loop evaluation workflows 2. Implement logging for prompts and responses 3. Evaluate model outputs for quality and bias 4. Monitor model performance and application metrics
Topic 3: 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 security, compliance, and responsible AI 1. Ensure compliance with AI ethics and responsible use 2. Implement data encryption and privacy controls 3. Implement IAM roles and policies for AI services 4. Implement content filtering and safety mechanisms - Implement data preprocessing and vectorization pipelines 1. Implement embeddings generation using AWS services 2. Implement document chunking and text processing 3. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora) 4. Ingest and transform data for model consumption

Section

Weight

Objectives

Topic 1: Plan and Design a Generative AI Application

25%

- Design the generative AI solution architecture

1. Design the end-to-end solution architecture
2. Design for scalability, reliability, and cost-effectiveness
3. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker)
4. Integrate with other AWS services (e.g., storage, databases, security)

- Select the appropriate foundation models (FMs) and techniques

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

- 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

Topic 2: Optimize and Operationalize a Generative AI Application

30%

- 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. Implement caching strategies for frequent queries
4. Optimize model selection and parameter tuning

- Implement monitoring, logging, and evaluation

1. Implement human-in-the-loop evaluation workflows
2. Implement logging for prompts and responses
3. Evaluate model outputs for quality and bias
4. Monitor model performance and application metrics

Topic 3: 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 security, compliance, and responsible AI

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

- Implement data preprocessing and vectorization pipelines

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q37-Q42):

NEW QUESTION # 37
An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations.
The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company's product catalog. The company must resolve these issues.
Which solution will meet this requirement?

A. Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput.
B. Store product catalog data in Amazon OpenSearch Service. Validate the model's product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.
C. Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers.
D. Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation RAG. Set the PerformanceConfigLatency parameter to optimized.

Answer: D

Explanation:
Option C best addresses both core problems: hallucinated recommendations that do not exist in the catalog and slow response times, while keeping operational overhead low. The most direct way to prevent the model from recommending unavailable products is to ground generation on authoritative product catalog data at inference time. An Amazon Bedrock knowledge base is designed for this pattern by ingesting domain data, chunking content, creating embeddings, and retrieving the most relevant catalog entries when a user asks for recommendations. Implementing Retrieval Augmented Generation ensures the foundation model receives only approved, catalog-backed context and can cite or base its output on those retrieved items. This sharply reduces the likelihood of inventing products, because the response is conditioned on retrieved catalog records rather than relying on the model's parametric memory.
The requirement also notes that most interactions are unique. That makes response caching far less effective, because there are fewer repeated prompts to benefit from cached outputs. Instead, improving the retrieval and model invocation path is the better optimization. Using the PerformanceConfigLatency parameter set to optimized prioritizes lower latency behavior for model inference, helping meet faster recommendation generation without requiring the company to build and operate additional infrastructure.
The other options do not solve the root cause as reliably. Prompt engineering and streaming can improve perceived latency, but they do not guarantee catalog-only recommendations because the model can still hallucinate items. Guardrails can help detect or block certain undesired outputs, but without consistent catalog grounding they do not ensure every recommendation is derived from the company's product data. Building a custom OpenSearch validation and caching layer increases operational complexity, and caching is misaligned with predominantly unique interactions.
Alright, after comparing List B (txt file) against List A (Word file) , I have identified the unique questions.
These questions cover scenarios or architectural configurations that were not present in the existing list.
Here are the unique questions from List B, formatted as requested:

NEW QUESTION # 38
A bank is developing a generative AI (GenAI)-powered AI assistant that uses Amazon Bedrock to assist the bank's website users with account inquiries and financial guidance. The bank must ensure that the AI assistant does not reveal any personally identifiable information (PII) in customer interactions.
The AI assistant must not send PII in prompts to the GenAI model. The AI assistant must not respond to customer requests to provide investment advice. The bank must collect audit logs of all customer interactions, including any images or documents that are transmitted during customer interactions.
Which solution will meet these requirements with the LEAST operational effort?

A. Use an AWS Lambda function and Amazon Comprehend to detect and redact PII. Use Amazon Comprehend topic modeling to prevent the AI assistant from discussing investment advice topics. Set up custom metrics in Amazon CloudWatch to capture customer conversations.
B. Use Amazon Macie to detect and redact PII in user inputs and in the model responses. Apply prompt engineering techniques to force the model to avoid investment advice topics. Use AWS CloudTrail to capture conversation logs.
C. Use regex controls to match patterns for PII. Apply prompt engineering techniques to avoid returning PII or investment advice topics to customers. Enable model invocation logging, delivery logging, and image logging to Amazon S3.
D. Configure Amazon Bedrock guardrails to apply a sensitive information policy to detect and filter PII.
Set up a topic policy to ensure that the AI assistant avoids investment advice topics. Use the Converse API to log model invocations. Enable delivery and image logging to Amazon S3.

Answer: D

Explanation:
Option C is the correct solution because Amazon Bedrock guardrails are purpose-built to enforce defense-in- depth safety controls for GenAI applications with minimal operational overhead. Guardrails provide managed, policy-based enforcement that operates before prompts are sent to the foundation model and after responses are generated, which directly satisfies the requirement that PII must not be sent to the model and must not appear in outputs.
By configuring a sensitive information policy, the application can automatically detect and redact PII in user inputs and model responses without building custom preprocessing pipelines. This approach is more reliable and scalable than regex or prompt engineering techniques, which are brittle and error-prone for sensitive data handling.
The topic policy capability in Amazon Bedrock guardrails allows the bank to explicitly block investment advice topics, ensuring regulatory compliance. This policy-based approach is safer and more auditable than attempting to steer the model only through prompt instructions.
Using the Converse API enables structured, standardized interactions with the model and supports consistent logging of requests and responses. Enabling delivery logging and image logging to Amazon S3 ensures that all customer interactions, including documents and images, are captured in a durable, auditable storage layer.
This directly supports compliance, regulatory audits, and forensic analysis.
Option A incorrectly relies on Amazon Macie, which is designed for data-at-rest discovery rather than real- time conversational filtering. Option B introduces custom Lambda pipelines and topic modeling, increasing operational complexity. Option D relies on regex and prompt engineering, which do not meet financial-grade compliance standards.
Therefore, Option C delivers the strongest security, governance, and auditability with the least operational effort.

NEW QUESTION # 39
A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company's data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.
The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application's decision-making processes and provide data classification capabilities.
Which solution will meet these requirements?

A. Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring. Create manual compliance reports for each regulatory jurisdiction.
B. Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region.
C. Use Amazon S3 Object Lock with Region-specific S3 bucket policies. Pre-process the data points within the Region based on geographic origin before sending the data points to Amazon Bedrock. Use Amazon Macie to classify the data. Use AWS CloudTrail immutable logs to audit the decision-making processes.
D. Deploy the application in each Region with local IAM policies. Use Amazon Bedrock cross-Region inference to distribute the workload. Use Amazon CloudWatch to log AI decision-making processes.
Manually track compliance certifications across Regions.

Answer: C

Explanation:
This scenario requires strict data residency, regional processing, classification, and auditable decision trails, which Option C addresses using AWS-native governance services.
Region-specific Amazon S3 buckets enforce geographic data boundaries. Amazon S3 Object Lock ensures immutability of stored data and logs, supporting regulatory retention and non-repudiation requirements. Pre- processing data within the same Region before invoking Amazon Bedrock ensures that inference and data handling do not cross continental boundaries.
Amazon Macie provides managed, automated data classification for sensitive data types such as PII and financial records, fulfilling the classification requirement without custom tooling.
AWS CloudTrail immutable logs provide comprehensive audit trails of all API calls, model invocations, and data access events, ensuring traceability of AI decision-making processes.
Option A violates residency rules through cross-Region inference. Option B does not provide data classification. Option D introduces high operational overhead and relies on manual compliance reporting.
Therefore, Option C is the most compliant, scalable, and operationally efficient solution for regionally governed GenAI workloads.

NEW QUESTION # 40
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?

A. Use custom user-defined metadata to store author information. Use S3 Object Lock retention periods for timestamps. Use S3 Event Notifications for domain classification.
B. Store document timestamps in Amazon S3 system metadata. Use S3 object tags for domain classification. Implement custom user-defined metadata to store author information.
C. Use S3 Inventory reports to track timestamps. Create S3 access points for domain classification. Store author information in S3 Storage Lens dashboards.
D. Set up S3 Object Lock with legal holds to track document timestamps. Use S3 object tags for author information. Implement S3 access points for domain classification.

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 # 41
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 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.
B. 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.
C. 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.
D. 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.

Answer: B

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 # 42
......

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