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Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.
Topic 4	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 5	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.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q15-Q20):

NEW QUESTION # 15
A logistics company is using Amazon Bedrock to build an autonomous routing agent that coordinates with APIs that support warehouse, shipping, and international customs operations. The agent must meet the following requirements:
* Break requests into reasoning steps.
* Retry failed tool calls with backoff.
* Stop retrying after three consecutive failures.
* Require human approval for shipments that are valued over $100,000.
* Use MCP to provide access to tools and new integrations without requiring code changes.
Which combination of solutions will meet these requirements? (Select THREE.)

A. Use Amazon API Gateway with AWS Lambda authorizers to validate tool requests and implement rate limiting. Implement custom retry logic with exponential backoff and a circuit breaker that halts retries after three consecutive failures.
B. Use a Choice state to route high-value shipments to a human approval workflow.
C. Use Task states in AWS Step Functions to orchestrate each reasoning step. Use retry configurations with exponential backoff to handle tool failures.
D. Use Amazon Bedrock AgentCore Gateway to convert the warehouse, shipping, and customs APIs into MCP-compatible tools.
E. Use Amazon Bedrock Guardrails to block tool invocations for shipments that exceed the $100,000 threshold until a human approves the shipment through a separate workflow.
F. Use Amazon Bedrock AgentCore with action groups for each API. Configure the agent ' s orchestration prompt to implement retry logic and human approval conditions.
G. Use AWS Lambda functions that use MCP client libraries to invoke tools. Implement custom retry logic and circuit breaker patterns in Lambda function code.

Answer: B,C,D

Explanation:
Amazon Bedrock AgentCore Gateway is specifically designed to expose existing APIs, Lambda functions, services, and MCP servers to agents through a standardized Model Context Protocol interface. AWS documents that Gateway can transform REST APIs represented by OpenAPI specifications into MCP- compatible tools and provide agents with a unified endpoint for tool discovery and invocation. This directly satisfies the requirement to add or expose warehouse, shipping, and customs capabilities through MCP without repeatedly modifying agent integration code.
AWS Step Functions is appropriate for deterministic workflow orchestration. Task states can represent individual reasoning or tool-execution stages, while built-in Retry configuration supports IntervalSeconds , MaxAttempts , and BackoffRate . These settings provide controlled retries with exponential backoff and a defined maximum retry budget rather than requiring bespoke retry code. AWS explicitly documents MaxAttempts as the mechanism that stops retry behavior after the configured limit is reached.
A Choice state supplies deterministic conditional routing. The shipment value can be evaluated and requests above $100,000 can be sent to an approval branch. The approval task can use the Step Functions callback/task- token integration, which pauses workflow execution until an external process-such as a human approval system-returns success or failure. AWS specifically identifies human approval as a callback-task use case.
D is weaker because critical retry and approval policy should not depend solely on probabilistic orchestration prompts. E and G introduce unnecessary custom operational logic. F misuses Guardrails: Bedrock Guardrails governs model input/output safety and policy filtering; it is not a general-purpose human workflow orchestrator for shipment authorization.

NEW QUESTION # 16
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations.
Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real- time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.
Which solution will meet these requirements?

A. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.
B. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations.
Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.
C. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.
D. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.

Answer: C

Explanation:
Option C is the correct solution because it provides near real-time monitoring, hallucination detection, and cost anomaly awareness using built-in Amazon Bedrock and Amazon CloudWatch capabilities, with minimal custom development.
By configuring Amazon Bedrock invocation logging with text output logging, the company captures detailed prompt and response data for auditing and analysis without building custom logging pipelines. This data is stored in Amazon S3, providing durable storage for compliance and retrospective investigation.
Using Amazon Bedrock guardrails with contextual grounding checks allows the application to automatically detect hallucinations by verifying whether generated summaries are grounded in the provided clinical documents. This is the AWS-recommended approach for hallucination detection in RAG and summarization workloads and avoids the need to maintain custom evaluation models or pipelines.
Creating Amazon CloudWatch anomaly detection alarms for InputTokenCount and OutputTokenCount metrics enables automatic detection of abnormal token usage patterns that often correlate with runaway prompts, inefficient summarization, or prompt injection attempts. Anomaly detection adapts dynamically to usage trends, making it more effective than static thresholds for early cost warnings.
Option A introduces batch analytics with Glue and Athena, which is not near real time and increases operational overhead. Option B requires managing evaluation jobs and Lambda-based notification logic.
Option D focuses on infrastructure-level monitoring and offline dashboards rather than near real-time GenAI quality and cost signals.
Therefore, Option C best meets the requirements with the least operational effort and maintenance overhead.

NEW QUESTION # 17
A GenAI developer is using Amazon Bedrock AgentCore to build an agentic AI application. The application orchestrates multiple foundation models (FMs) across development and production environments. The application experiences intermittent failures during tool invocation phases. Each incident requires an average of 3 hours to diagnose because of complex error patterns in the FM chains.
The GenAI developer needs a solution that can identify AI-specific error patterns in tool invocation chains in less time than the existing manual process. The solution must support collaboration between development and security teams. The solution must provide capabilities to protect sensitive customer data.
Which solution will meet these requirements?

A. Set up AWS X-Ray tracing for all AWS Lambda functions that handle model invocations. Use Amazon OpenSearch Service to create a custom monitoring solution with OpenSearch dashboard visualizations.
B. Enable Amazon Bedrock AgentCore Observability with AWS Distro for OpenTelemetry instrumentation. Use Amazon CloudWatch Generative AI Observability to perform trace analysis and to monitor tool invocations.
C. Create an Amazon CloudWatch dashboard that displays detailed Amazon Bedrock API metrics.
Integrate the dashboard with Amazon GuardDuty. Use CloudWatch Logs Insights to perform error analysis.
D. Use Kiro as the integrated development environment (IDE) to directly access both application codebases and trace logs. Enable real-time analysis of error patterns directly in the IDE by analyzing both application code and trace logs together to understand how the agents orchestrate tool invocations.

Answer: B

Explanation:
Option D is correct because AgentCore Observability is purpose-built for tracing and troubleshooting agentic workloads. AgentCore traces capture the complete execution path, including processing steps, external service calls, tool invocations with inputs and outputs, execution times, decision points, exceptions, recovery attempts, and final responses. CloudWatch Generative AI Observability provides preconfigured views for agent, model, tool, latency, token, and error behavior, allowing development and security teams to investigate failures from shared telemetry instead of correlating several independent logging systems. AWS Distro for OpenTelemetry provides standardized instrumentation for non-runtime agents and related application components. CloudWatch also provides sensitive-data protection capabilities for observability data. Options A and C provide infrastructure-focused monitoring but require more manual correlation and custom dashboards. Option B does not provide the same production-grade centralized AgentCore telemetry. AWS Documentation

NEW QUESTION # 18
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?

A. Configure SAML federation between Microsoft Entra ID and AWS Identity and Access Management.
Create department-specific IAM roles that allow only the required ModelId values. Create AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services. Enable AWS CloudTrail to capture Amazon Bedrock API calls. Configure Amazon Bedrock model invocation logging to record detailed model interactions.
B. Configure OpenID Connect (OIDC) federation between Microsoft Entra ID and IAM. Use attribute- based access control to map department attributes to specific model access permissions. Apply SCP policies to restrict access to Amazon Bedrock FM families based on department. Use Microsoft Entra ID's built-in logging capabilities to maintain an audit trail of model interactions.
C. Create an identity provider (IdP) connection in IAM to authenticate by using Microsoft Entra ID.
Assign department permission sets to control access to specific model families. Deploy AWS Lambda functions in private subnets with a NAT gateway for egress to Amazon Bedrock public endpoints.
Enable CloudWatch Logs to capture model interactions for auditing purposes.
D. Create a SAML identity provider (IdP) in IAM to authenticate by using Microsoft Entra ID. Use IAM permissions boundaries to limit department roles' access to specific model families. Configure public Amazon Bedrock API endpoints with VPC routing to maintain private network connectivity. Set up CloudTrail with Amazon S3 Lifecycle rules to manage audit logs of model interactions.

Answer: A

NEW QUESTION # 19
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 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.
D. 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.

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 # 20
......

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