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Amazon AIP-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Generative AI Developer - Professional
Exam Number:	AIP-C01
Certificate Validity Period:	3 years
Real Exam Qty:	85
Exam Duration:	180 minutes
Available Languages:	English
Exam Format:	Ordering, Multiple Choice, Multiple Response, Matching
Passing Score:	750 (on a scale of 100-1000)
Related Certifications:	AWS Certified AI Practitioner
Exam Price:	$300 USD
Sample Questions:	Amazon AIP-C01 Sample Questions
Exam Way:	Online proctored exam or test center (Pearson VUE)
Pre Condition:	Recommended 3+ years of experience in software development and 1+ year of experience building applications with generative AI on AWS. AWS Cloud Practitioner certification or equivalent cloud knowledge recommended.
Official Syllabus URL:	https://aws.amazon.com/certification/certified-generative-ai-developer-professional/

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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	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 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.

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

NEW QUESTION # 15
A company is using Amazon Bedrock to build an AI assistant to help internal teams analyze unstructured customer feedback data. The company stores the customer feedback in an Amazon S3 bucket. The S3 bucket contains more than 25 TB of historical data from mobile app reviews, chat conversations, and call center transcripts. The company expects the data source to grow by 3 GB every day. The data entries often contain multiple unrelated topics within the same input.
The company needs a solution that reliably delivers accurate answers to questions based on the data source.
The solution must not export any personally identifiable information (PII) to the Amazon Bedrock model during processing or response generation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an Amazon Bedrock knowledge base that synchronizes with the S3 bucket by using semantic chunking. Configure the knowledge base to use an Amazon Aurora PostgreSQL vector store. Configure an Amazon Bedrock guardrail to block all types of PII during input and output processing. Configure Amazon Bedrock AgentCore to use the knowledge base and the guardrail to process and answer queries.
B. Configure an Amazon Bedrock knowledge base that synchronizes with the S3 bucket by using fixed- size chunking. Configure the knowledge base to use an Amazon Aurora PostgreSQL vector store.
Configure an Amazon Bedrock guardrail to block all types of PII during input and output processing.
Configure Amazon Bedrock AgentCore to use the knowledge base and the guardrail to process and answer queries.
C. Configure an AWS Lambda function that processes each new file in the S3 bucket to detect and remove PII by using Amazon Comprehend. Configure the function to generate fixed-size chunk embeddings and store them in an Amazon OpenSearch Serverless vector store. Configure a second Lambda function to process questions, retrieve context, and invoke an Amazon Bedrock foundation model directly to generate answers.
D. Configure an Amazon Bedrock knowledge base that synchronizes with the S3 bucket by using semantic chunking. Configure the knowledge base to use an Amazon OpenSearch Serverless vector store.
Configure an Amazon Bedrock guardrail to block all types of PII during input and output processing.
Configure Amazon Bedrock AgentCore to use the knowledge base and the guardrail to process and answer queries.

Answer: C

Explanation:
Option A is the only listed choice that satisfies the hard requirement that PII must not reach the Bedrock model. AWS explicitly states that Knowledge Base guardrails apply to the user input and generated model response, but not to references retrieved from the Knowledge Base at runtime. Therefore, Options B, C, and D can still place PII-bearing retrieved chunks into model context even though a guardrail is configured. Amazon Comprehend can detect PII, enabling the Lambda preprocessing step in A to remove sensitive data before embeddings are created and before retrieved context is ever sent to the foundation model. Although semantic chunking would normally be preferable for records containing multiple unrelated topics, privacy is a mandatory constraint. Option A has more custom processing, but it is the only option that enforces PII removal upstream of model invocation.

NEW QUESTION # 16
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. 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.
B. 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.
C. 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.
D. 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.

Answer: D

NEW QUESTION # 17
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 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.
B. 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.
C. 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.
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: C

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 # 18
A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company ' s engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company ' s marketing team tracks business metrics such as conversion rates and revenue impact in its own systems. The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics. Which solution will meet these requirements?

A. Configure CloudWatch custom dashboards that integrate operational metrics with imported business metrics. Set up CloudWatch composite alarms with anomaly detection. Use Amazon SNS to create alarm actions to notify stakeholders when correlated metrics indicate performance issues.
B. Use Amazon Managed Grafana to visualize technical metrics from CloudWatch with business metrics from external sources. Configure Amazon Managed Grafana alerts to invoke AWS Lambda functions.
Configure the Lambda functions to remediate issues automatically when metrics exceed predefined thresholds.
C. Create CloudWatch dashboards that include technical metrics and imported business metrics. Configure CloudWatch composite alarms that combine technical data and business data. Use Amazon SNS to set up notifications to stakeholders.
D. Stream CloudWatch metrics to Amazon S3 by using CloudWatch metric streams. Create Amazon QuickSight dashboards to visualize the combined technical metrics and business metrics. Set up Amazon EventBridge rules to send notifications to stakeholders when metrics exceed predefined thresholds.

Answer: A

Explanation:
Amazon CloudWatch provides the most integrated path for unifying technical and business metrics. By importing business metrics into CloudWatch (via custom metrics or metric streams), teams can build custom dashboards that provide a single pane of glass for both system health and conversion performance.
Composite alarms allow stakeholders to be notified only when multiple conditions are met (e.g., high latency and dropping conversion rates), reducing alert fatigue. Applying anomaly detection to these metrics is essential for GenAI workloads because performance baselines can shift subtly; CloudWatch can automatically detect these deviations and trigger alerts through Amazon SNS . This solution provides comprehensive correlation and automated alerting with less operational complexity than managing external visualization servers (Option B) or multi-service analytics pipelines (Option C).

NEW QUESTION # 19
A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block harmful content, mask personally identifiable information (PII), and maintain audit trails for compliance reporting. The AI assistant must apply content filtering to both user inputs and model responses based on content sensitivity.
The company requires an Amazon Bedrock guardrail configuration that will effectively enforce policies with minimal false positives. The solution must provide multiple handling strategies for multiple types of sensitive content.
Which solution will meet these requirements?

A. Configure a single guardrail and set content filters to high for all categories. Set up denied topics for investment advice and include sample phrases to block. Set up sensitive information filters that apply the block action for all PII entities. Apply the guardrail to all model inference calls.
B. Create a separate guardrail for each use case. Create one guardrail that applies a harmful content filter.Create a guardrail to apply topic filters for investment advice. Create a guardrail to apply sensitive information filters to block PII. Use AWS Step Functions to chain the guardrails sequentially.
C. Configure a guardrail and set content filters to medium for harmful content. Set up denied topics for investment advice and include clear definitions and sample phrases to block. Configure sensitive information filters to mask PII in responses and to block financial information in inputs. Enable both input and output evaluations that use custom blocked messages for audits.
D. Configure multiple guardrails by using tiered policies. Create one guardrail and set content filters to high. Configure the guardrail to block PII for public interactions. Configure a second guardrail and set content filters to medium. Configure the second guardrail to mask PII for internal use. Configure multiple topic-specific guardrails to block investment advice and set up contextual grounding checks.

Answer: C

Explanation:
Option C is the correct solution because it uses a single, well-tuned Amazon Bedrock guardrail that applies different actions to different content types, which is the recommended approach for minimizing false positives while enforcing strong policy controls.
Setting content filters to medium rather than high reduces overblocking of benign customer conversations while still preventing harmful content. Amazon Bedrock guardrails are designed to balance precision and recall, and medium sensitivity is commonly recommended for customer-facing financial services use cases.
Denied topics explicitly prevent the assistant from discussing investment advice, which is a regulatory requirement. Including definitions and sample phrases improves detection accuracy and reduces ambiguity.
Sensitive information filters support different actions per context. Masking PII in responses preserves conversational usefulness for legitimate customer support while preventing exposure of sensitive data.
Blocking sensitive financial information in inputs prevents downstream processing of disallowed content before it reaches the foundation model.
Critically, enabling both input and output evaluation ensures that guardrails are applied consistently at every stage of interaction. Custom blocked messages and audit logging provide clear compliance evidence for regulators and internal audits.
Option A causes excessive false positives by blocking all PII outright. Option B introduces unnecessary complexity and is not how Bedrock guardrails are intended to be applied. Option D uses orchestration logic that Bedrock guardrails already handle natively.
Therefore, Option C best satisfies enforcement, flexibility, auditability, and accuracy requirements.

NEW QUESTION # 20
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