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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
AI Safety, Security, and Governance	20%	- Secure generative AI systems 1. Threat modeling and vulnerability management 2. Model security, data protection, and encryption - Implement safety and guardrails 1. Prevent harmful outputs and misuse 2. Content filtering, moderation, and risk control - Establish governance frameworks 1. Lifecycle management and change control 2. Policies, standards, and audit processes
Foundation Model Integration, Data Management, and Compliance	31%	- Analyze requirements and design generative AI solutions 1. Architectural design aligned with business goals 2. Select appropriate foundation models and services - 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
Testing, Validation, and Troubleshooting	11%	- Validate quality and reliability 1. Output accuracy, relevance, and consistency 2. Functional and non-functional testing - Troubleshoot and resolve issues 1. Debugging and performance tuning 2. Diagnose errors in integration, deployment, or data
Implementation and Integration	26%	- 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. Custom model deployment and fine-tuning 2. Prompt engineering and optimization - Integrate foundation models into applications and workflows 1. API integration and workflow orchestration 2. Use AWS services like Amazon Bedrock, SageMaker
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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q67-Q72):

NEW QUESTION # 67
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?

A. Cache prompt results in Amazon ElastiCache. Use AWS Lambda functions to pre-process metadata and to trace end-to-end latency. Use AWS X-Ray to identify and remediate performance bottlenecks.
B. Use Amazon Bedrock Agents to manage chaining. Log model inputs and outputs to Amazon CloudWatch Logs. Use logs from CloudWatch to perform A/B testing for prompt versions.
C. Deploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply Amazon Bedrock guardrails with semantic denial rules to block unsafe outputs. Use Amazon Bedrock Prompt Management to manage prompts by using approval workflows.
D. Use Amazon Kendra to improve roast log retrieval accuracy. Store normalized prompt metadata within Amazon DynamoDB. Use AWS Step Functions to orchestrate multi-step prompts.

Answer: C

Explanation:
Option A is the only choice that simultaneously addresses all three requirements: (1) higher output consistency for identical inputs, (2) sub-1-second performance, and (3) validated safety controls that block unsafe or hallucinated recommendations.
Provisioned throughput in Amazon Bedrock reserves capacity for the chosen model, which helps stabilize latency and reduces the chance of throttling or variable response times across Regions. This is important for a mobile app with strict latency goals and users distributed across multiple Regions. While provisioned throughput primarily improves performance predictability, it also reduces variability caused by contention during peak demand.
Amazon Bedrock guardrails provide validated safety controls to filter or block unsafe content. Semantic denial rules are appropriate for preventing dangerous brewing guidance (for example, excessively high temperatures) and for reducing hallucinated instructions that violate safety policies. Guardrails can be enforced consistently regardless of prompt-chain complexity, providing a uniform safety layer around the model outputs.
Amazon Bedrock Prompt Management supports controlled prompt versioning and approval workflows. By standardizing prompts, controlling changes, and ensuring the same prompt version is used for identical inputs, the company improves output stability and reduces drift caused by unmanaged prompt edits. Combined with strict configuration control (including fixed inference parameters such as temperature where appropriate), this improves repeatability and increases the likelihood of achieving the 99.5% consistency target.
Option B improves observability and experimentation but does not provide strong safety enforcement or latency stabilization. Option C improves performance through caching and tracing but does not provide validated safety controls and does not directly address cross-Region output consistency. Option D may improve retrieval but does not enforce safety controls or ensure repeatable outputs.
Therefore, Option A best meets the stability, performance, and safety requirements using AWS-native controls.

NEW QUESTION # 68
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. 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.
B. 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.
C. 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.
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: 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 # 69
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?

A. Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.
B. Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.
C. Configure an AWS Lambda function to fetch routing configuration from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
D. Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.

Answer: C

NEW QUESTION # 70
A healthcare company runs a multi-step workflow that uses Amazon Bedrock foundation models (FMs) to assist clinicians with making patient clinical decisions. Recent data shows that 25% of decision processes fail to finish. However, the company cannot identify which specific workflow step is causing the failures.
The company needs to implement a monitoring solution to identify the exact failure points in the workflow.
The solution must provide detailed analytics for the completion rates of each workflow step.
Which solution will meet these requirements?

A. Enable tracing for InvokeAgent API requests to capture OrchestrationTrace and FailureTrace objects.
Develop analytics on the structured trace data to calculate completion rates for each workflow step.
B. Enable AWS CloudTrail logging for the Amazon Bedrock FM workflow. Create scheduled Amazon Athena queries that analyze log patterns to identify which steps of the workflow fail most frequently.
C. Configure Amazon CloudWatch alarms to monitor the workflow ' s InvocationServerErrors API and InvocationClientErrors API error rates. Set up notifications for when errors exceed 25%. Configure custom metrics to track each workflow step separately.
D. Use Amazon EventBridge to capture workflow events for state transitions. Store the event data in Amazon S3. Use Amazon QuickSight to create dashboards that display completion rates for each workflow step.

Answer: A

Explanation:
Option C is correct because Amazon Bedrock agent tracing exposes the internal execution path needed to identify precisely where a multi-step workflow fails. When tracing is enabled for InvokeAgent, the response can include structured trace events. AWS documents OrchestrationTrace for orchestration activity and FailureTrace for the reason a particular step failed. Those records can be aggregated to calculate per-step attempts, successes, failures, and completion rates. Option A monitors broad client/server error metrics; even with custom metrics, it does not inherently provide the same structured execution context. Option B uses CloudTrail, which is primarily an API audit trail rather than a step-by-step agent reasoning trace. Option D requires custom EventBridge instrumentation and storage. Native agent trace data therefore provides the most direct evidence for exact failure-point analysis and detailed workflow completion analytics. AWS Documentation

NEW QUESTION # 71
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 input/output records.
Which solution will meet these requirements?

A. Use AWS Glue Data Catalog with crawlers to maintain data sources. Store generated summaries in Amazon S3. Write object tags that include a source ID. Store Amazon Bedrock model invocation logs in Amazon S3. Enable S3 Object Lock on the S3 bucket that stores invocation logs. Use AWS CloudTrail log file integrity validation to provide tamper-evident immutability.
B. Use Amazon Comprehend to identify data sources in the documents. Store generated summaries in Amazon S3 and enable S3 Object Lock. Use Amazon CloudWatch metrics to generate reports about application throughput. Do not include logs for each invocation.
C. Store application outputs in Amazon DynamoDB. Apply item-level tags that include source attribution.
Write application events to Amazon CloudWatch Logs. Use IAM roles to provide audit traceability.
D. Use AWS AppConfig feature flags to implement data versioning. Restrict access to the model by using IAM condition keys. Maintain a versioned mapping file of source-to-output relationships in Amazon S3.

Answer: A

Explanation:
Option B is correct because it is the only option that satisfies all requirements: centralized cataloging, automatic data-source discovery, citation metadata, model input/output logging, and tamper-evident immutable audit retention. AWS Glue Data Catalog is designed as a central metadata repository, and AWS Glue crawlers can automatically discover and catalog new or updated data sources. AWS documentation states that crawlers can infer metadata and keep the Data Catalog up to date across sources such as Amazon S3, Amazon RDS, Amazon Redshift, and other supported stores. This directly addresses the requirement to catalog all data sources centrally and update them automatically.
Storing generated summaries in Amazon S3 and writing object tags that include source IDs is appropriate because S3 object tags are queryable metadata that can be used for organization, lifecycle policies, access controls, cost allocation, and downstream indexing. For GenAI traceability, the source ID tag can represent the citation or provenance link from the generated summary back to the original technical document.
Amazon Bedrock model invocation logging is also required here because the company must retain input
/output records for every model invocation. AWS documentation states that model invocation logging can collect invocation logs, model input data, and model output data for Amazon Bedrock invocations, with Amazon S3 as a supported delivery destination.
For immutable retention, S3 Object Lock provides write-once-read-many protection and helps prevent objects from being deleted or overwritten for a fixed period or indefinitely. CloudTrail log file integrity validation adds tamper evidence by using hashing and digital signing to detect whether log files were changed, deleted, or forged after delivery.
Option A explicitly omits logs for each invocation, so it fails the audit requirement. Option C lacks automatic source discovery and immutable audit retention. Option D uses configuration tooling, not a cataloging and audit architecture. Therefore, option B is the complete AWS-aligned solution.

NEW QUESTION # 72
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