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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Operational Efficiency and Optimization for GenAI Applications	12%	- Optimize performance and cost 1. Cost management and right-sizing 2. Latency, throughput, and resource utilization - Deploy and manage scalable solutions 1. Infrastructure deployment, automation, and IaC 2. Monitoring, logging, and observability
AI Safety, Security, and Governance	20%	- Establish governance frameworks 1. Lifecycle management and change control 2. Policies, standards, and audit processes - Secure generative AI systems 1. Model security, data protection, and encryption 2. Threat modeling and vulnerability management - Implement safety and guardrails 1. Content filtering, moderation, and risk control 2. Prevent harmful outputs and misuse
Implementation and Integration	26%	- Integrate foundation models into applications and workflows 1. API integration and workflow orchestration 2. Use AWS services like Amazon Bedrock, SageMaker - Integrate with existing systems and services 1. Integration with compute, storage, and networking 2. Identity, security, and access control - Implement generative AI features and capabilities 1. Prompt engineering and optimization 2. Custom model deployment and fine-tuning
Testing, Validation, and Troubleshooting	11%	- Troubleshoot and resolve issues 1. Diagnose errors in integration, deployment, or data 2. Debugging and performance tuning - Validate quality and reliability 1. Functional and non-functional testing 2. Output accuracy, relevance, and consistency
Foundation Model Integration, Data Management, and Compliance	31%	- Manage data for generative AI applications 1. Vector databases and retrieval-augmented generation (RAG) 2. Data preparation, ingestion, and transformation - Ensure compliance and responsible AI practices 1. Bias mitigation, interpretability, and transparency 2. Data privacy, security, and regulatory adherence - Analyze requirements and design generative AI solutions 1. Select appropriate foundation models and services 2. Architectural design aligned with business goals

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q27-Q32):

NEW QUESTION # 27
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?

A. Use S3 object metadata to store each document ' s timestamp. Use a custom metadata field to record authorship. Use S3 object tags to record the research domain. Propagate the metadata fields into the OpenSearch Serverless vector collection as filterable attributes. Use Knowledge Bases to apply timestamp, author, and domain filters before running vector similarity search.
B. Add custom metadata fields to the documents in Amazon S3 to record timestamp, authorship, and research domain. Index the document embeddings and the custom metadata fields into the OpenSearch Serverless vector collection. At query time, use the knowledge base to run vector similarity search and return the stored metadata with the results to help the model interpret document relevance.
C. Store documents in Amazon S3. Extract timestamp, author metadata, and research-domain tags, and store the data in an Amazon DynamoDB table. During retrieval, apply author, domain, and timestamp filters in DynamoDB to identify candidate document IDs. Use the filtered document IDs to narrow the vector similarity search in the OpenSearch Serverless collection.
D. Store documents in Amazon S3 with custom metadata to record authorship. Use Amazon Comprehend to classify each document into a research domain. Store the classification results in Amazon Aurora.Query Aurora during retrieval to identify relevant domains before performing vector similarity search in the OpenSearch Serverless collection.

Answer: A

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 # 28
A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches.
Which solution will meet these requirements with the LEAST custom development effort?

A. Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants.Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.
B. Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics.
C. Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS Lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.
D. Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension by using InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.

Answer: D

Explanation:
Option B best satisfies the requirements with the least custom development effort by using native Amazon Bedrock capabilities for prompt experimentation, traffic management, fairness monitoring, and alerting.
Amazon Bedrock Prompt Management allows teams to define and manage multiple prompt variants without code changes, making it ideal for comparing recommendation strategies across demographic groups.
Amazon Bedrock Flows enables controlled traffic allocation between prompt variants, which supports real- time A/B testing. This allows the company to collect live fairness metrics under production conditions instead of relying on offline analysis. Because Flows are fully managed, they eliminate the need for custom routing or experimentation frameworks.
Amazon Bedrock guardrails provide built-in monitoring and intervention mechanisms. When configured for fairness-related checks, guardrails can detect policy violations and surface metrics such as InvocationsIntervened, which indicate when outputs are modified or blocked due to rule enforcement. These metrics integrate directly with Amazon CloudWatch, enabling real-time dashboards and threshold-based alarms. Setting an alarm at a 15% discrepancy threshold satisfies the alerting requirement with minimal configuration.
Weekly reporting can be generated from CloudWatch metrics using scheduled exports or dashboards without building custom analytics pipelines. Option A requires significant custom post-processing logic. Option C introduces an additional service with higher operational overhead and is not optimized for real-time monitoring. Option D focuses on offline evaluation jobs and does not provide continuous real-time fairness monitoring.
Therefore, Option B provides the most AWS-native, scalable, and low-effort solution for fairness evaluation and monitoring.

NEW QUESTION # 29
A company is building a generative AI (GenAI) application that uses Amazon Bedrock APIs to process complex customer inquiries. During peak usage periods, the application experiences intermittent API timeouts that cause issues such as broken response chunks and delayed data delivery. The application struggles to ensure that prompts remain within token limits when handling complex customer inquiries of varying lengths.
Users have reported truncated inputs and incomplete responses. The company has also observed foundation model (FM) invocation failures.
The company needs a retry strategy that automatically handles transient service errors and prevents overwhelming Amazon Bedrock during peak usage periods. The strategy must also adapt to changing service availability and support response streaming and token-aware request handling.
Which solution will meet these requirements?

A. Set Amazon Bedrock client request timeouts to 30 seconds. Implement client-side load shedding.Buffer partial results and stop new requests when application performance degrades. Set static token usage caps for all requests. Configure exponential backoff retries, dynamic chunk sizing, and context- aware token limits.
B. Implement a standard retry strategy that uses a 1-second fixed delay between attempts and a 3-retry maximum for all errors. Handle streaming response timeouts by restarting streams. Cap token usage for each session.
C. Use the AWS SDK to configure a retry strategy in standard mode. Wrap Amazon Bedrock API calls in try-catch blocks that handle timeout exceptions. Return cached completions for failed streaming requests. Enforce a global token limit for all users. Add jitter-based retry logic and lightweight token trimming for each request. Resume broken streams by requesting only missing chunks from the point of failure. Maintain a small in-memory buffer of the most recent chunks.
D. Implement an adaptive retry strategy that uses exponential backoff with jitter and a circuit breaker pattern that temporarily disables retries when error rates exceed a predefined threshold. Implement a streaming response handler that monitors for chunk delivery timeouts. Configure the handler to buffer successfully received chunks and intelligently resume streaming from the last received chunk when connections are re-established.

Answer: D

Explanation:
Option B best meets all requirements because it combines AWS-recommended resiliency patterns for transient failures with streaming-aware handling and adaptive protection against cascading retries during peak load. When timeouts and throttling occur, naive retries can amplify traffic and worsen outages. Exponential backoff with jitter is the standard AWS best practice because it spreads retry attempts over time, reduces synchronized retry storms, and lowers the probability of repeatedly colliding with service limits.
The requirement also states the strategy must "adapt to changing service availability" and "prevent overwhelming Amazon Bedrock." A circuit breaker pattern directly addresses this by temporarily stopping or reducing retries when failure rates exceed a threshold, allowing the system to degrade gracefully instead of continually hammering the service. This is a key mechanism to prevent cascading failures during throttling events.
Because the application uses response streaming and experiences broken chunks, the retry strategy must be streaming-aware. A streaming response handler that detects chunk delivery timeouts and buffers already received chunks prevents the user from losing progress when a connection drops. Resuming from the last successfully received chunk minimizes redundant generation and reduces additional load on the model compared with restarting the entire stream. This supports better user experience and better service efficiency during intermittent failures.
Token-aware request handling is supported in this architecture because the application can apply token budgeting before invoking the model (for example, trimming or summarizing excessive context) while still preserving streaming output behavior. Option B provides the correct backbone for this by focusing on adaptive control and robust streaming recovery.
Option A is too simplistic and risks retry storms. Option C combines conflicting elements (global token limit, cached completions for streaming) and includes impractical "request only missing chunks" behavior that is not a reliable property of streamed generative output. Option D includes useful ideas (load shedding) but relies on static caps and does not provide as strong adaptive retry control as circuit breaking.
Therefore, Option B is the most correct and operationally safe strategy for peak-load Bedrock streaming workloads.

NEW QUESTION # 30
A financial services company needs to pre-process unstructured data such as customer transcripts, financial reports, and documentation. The company stores the unstructured data in Amazon S3 to support an Amazon Bedrock application.
The company must validate data quality, create auditable metadata, monitor data metrics, and customize text chunking to optimize foundation model (FM) performance.
Which solution will meet these requirements with the LEAST development effort?

A. Use Amazon SageMaker Data Wrangler to create a data flow. Configure Amazon CloudWatch metrics and alarms to monitor data quality. Use a custom AWS Lambda function to pre-process the data. Load processed data into Amazon Bedrock.
B. Create an AWS Step Functions workflow to orchestrate data pre-processing tasks. Run custom code on Amazon EC2 instances. Use Amazon SageMaker Model Monitor to monitor data quality. Load processed data into Amazon Bedrock.
C. Use Amazon Comprehend to extract entities. Create an AWS Lambda function to chunk text. Run Amazon Athena to query and validate data quality. Load processed data into Amazon Bedrock.
D. Set up an AWS Glue crawler to catalog data sources. Create AWS Glue ETL jobs to run custom transformation scripts. Use AWS Glue Data Quality to validate and monitor data quality. Load processed data into Amazon Bedrock.

Answer: D

Explanation:
Option B is the most appropriate solution because it uses AWS-native, purpose-built data engineering and governance services to address data quality validation, metadata creation, monitoring, and transformation with minimal custom development. AWS Glue is designed specifically for large-scale data preparation and integrates seamlessly with Amazon S3, making it ideal for preprocessing unstructured datasets for downstream GenAI applications.
AWS Glue crawlers automatically infer schemas and populate the AWS Glue Data Catalog, creating auditable, queryable metadata for all datasets. This satisfies the requirement for traceability and governance, which is especially critical in financial services environments. Glue ETL jobs allow teams to implement customizable transformation logic, including text normalization and chunking strategies optimized for foundation model context windows.
AWS Glue Data Quality provides built-in rulesets for validating completeness, accuracy, and consistency. It also publishes quality metrics that can be monitored over time, meeting the requirement for ongoing data quality monitoring without building custom validation frameworks.
Because AWS Glue is fully managed, it eliminates the need to manage infrastructure, scaling, or orchestration. This significantly reduces development and operational effort compared to custom Lambda pipelines or EC2-based processing. The processed and validated data can then be safely ingested into Amazon Bedrock workflows or knowledge bases.
Option A and C require custom logic for validation, monitoring, and chunking, increasing development complexity. Option D introduces unnecessary infrastructure management and services not optimized for data preprocessing.
Therefore, Option B best meets the requirements while minimizing development effort and aligning with AWS Generative AI data preparation best practices.

NEW QUESTION # 31
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.)

A. Create an SCP that allows employees to use only approved models. Configure the SCP to require employees to specify a guardrail identifier in calls to invoke an approved model.
B. Create an SCP that prevents an employee from invoking a model if a centrally deployed guardrail identifier is not specified in a call to the model. Create a permissions boundary on each employee's IAM role that allows each employee to invoke only approved models.
C. Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a mask filtering policy. Use stack sets to deploy the guardrail to each account in the organization.
D. Create an IAM permissions boundary for each employee's IAM role. Configure the permissions boundary to require an approved Amazon Bedrock guardrail identifier to invoke Amazon Bedrock models. Create an SCP that allows employees to use only approved models.
E. Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a block filtering policy. Use stack sets to deploy the guardrail to each account in the organization.

Answer: B,E

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 # 32
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