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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
Exam Duration:	180 minutes
Real Exam Qty:	75 (65 scored + 10 unscored)
Exam Price:	300 USD
Exam Format:	Multiple response, Matching, Ordering, Multiple choice
Certificate Validity Period:	3 years
Related Certifications:	AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty
Passing Score:	750 (scaled score 100–1000)
Available Languages:	Korean, Japanese, Simplified Chinese, English
Recommended Training:	AWS Skill Builder - Official Training AWS Certified Generative AI Developer - Professional Exam Guide
Exam Registration:	Pearson VUE Registration AWS Certification Registration
Sample Questions:	Amazon AIP-C01 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE test centers
Pre Condition:	2+ years of experience building production-grade applications on AWS or open-source technologies; 1+ year hands-on experience with generative AI implementation; knowledge of AWS compute, storage, networking, security, and deployment tools
Official Syllabus URL:	https://docs.aws.amazon.com/aws-certification/latest/examguides/ai-professional-01.html

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Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 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 (Q50-Q55):

NEW QUESTION # 50
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. 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.
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. 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.

Answer: B

NEW QUESTION # 51
A logistics company is building an agentic GenAI-powered solution to automate freight optimization. The solution must retrieve data in real time from multiple internal and external systems. The solution must include a human-in-the-loop approval step before the optimization process is finished. The solution must support modular growth as the number of integrations and amount of logic increases.
Which solution will meet these requirements with the LEAST operational overhead?

A. Build a hierarchical system by using the Strands Agents SDK and Amazon Bedrock AgentCore.
Configure a coordinating agent to delegate tasks to multiple specialized agents. Use MCP to facilitate inter-agent messaging. Use AWS Step Functions to implement a human-in-the-loop approval step.
B. Use AWS Glue to aggregate operational data into Amazon S3. Use Amazon Athena to query the data.
Invoke an AWS Lambda function to generate route assignments. Use Amazon SNS to send notifications to supervisor agents.
C. Use a single Amazon Bedrock AgentCore agent with AWS Lambda-based tools to integrate with all internal and external systems. Use AWS Step Functions to orchestrate the approval workflow.
D. Use an Amazon SageMaker AI endpoint that hosts a large language model (LLM) that directly calls all internal databases and external APIs. Use a custom web application that provides a UI to implement the human-in-the-loop review step.

Answer: A

Explanation:
Option B provides the strongest modular architecture for an integration-heavy agentic workload. Strands supports hierarchical agents-as-tools patterns in which a coordinating agent delegates specialized work to independent agents, allowing integrations and domain logic to be added or modified without creating one increasingly complex agent. AgentCore Gateway exposes tools through standardized MCP interfaces so agents can discover and invoke internal or external capabilities through a unified connectivity layer. AWS Step Functions supports callback tasks that pause execution until a human or external process returns a task token, making it suitable for the required human approval checkpoint. Option A requires custom model hosting and approval UI development. Option C is primarily a batch data-processing architecture rather than real-time agent orchestration. Option D can function initially, but a single agent becomes less modular and maintainable as the number of tools and business domains expands. Strands Agents

NEW QUESTION # 52
A retail company runs an application that makes product recommendations to customers on the company's website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear in the responses, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement?

A. Use an Amazon Bedrock Guardrails filter on the prompt. Set the input filter strength to HIGH.
B. Run the prompt through Amazon Bedrock. Process the response through Amazon Bedrock AgentCore to add the promotional message. Rerank the results by using the original prompt and the desired message as context.
C. Reinforce the requirement to include the new promotional message within product recommendations by using an output indicator in prompts to the FM.
D. Generate multiple response variants that include the promotional message in different ways. Use a reranker model to select the most coherent version based on relevance to the original prompt.

Answer: C

Explanation:
When a foundation model fails to include specific required content or fails to integrate it coherently, prompt engineering techniques like output indicators or " wrappers " are highly effective. By explicitly defining where the promotional message should appear (e.g., " The response must end with the following message:
[PROMO TEXT] " ) or providing an example output structure, the developer reinforces the constraint within the model ' s generation path. This is more direct and less computationally expensive than generating multiple variants and reranking them (Option B) or adding complex post-processing layers (Option C). Guardrails (Option A) are intended for filtering harmful content rather than enforcing specific promotional copy insertion.

NEW QUESTION # 53
A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.
Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.
Which solution will meet these requirements?

A. Associate one Amazon Bedrock AgentCore agent with two knowledge bases. Configure one knowledge base to access documents from Amazon S3 and the other to access structured data from Amazon RDS.
Configure the agent to preserve attribution metadata in responses. Use IAM roles to control access to sensitive data.
B. Configure one Amazon Bedrock AgentCore agent to use a knowledge base that contains the documents from Amazon S3. Configure AWS Lambda functions to query Amazon RDS directly for structured data and to combine the results before returning responses.
C. Create two Amazon Bedrock AgentCore agents. Associate one agent with a knowledge base that is connected to the documents in Amazon S3 and the other agent with a knowledge base that is connected to Amazon RDS. Develop a custom AWS Lambda function to query both agents simultaneously and combine the results.
D. Create one Amazon Bedrock AgentCore agent that uses AWS Lambda functions to directly query Amazon S3 for documents and Amazon RDS for structured data. Process the responses and merge the results into a single response.

Answer: A

Explanation:
Option A best matches the requirement because managed knowledge-base retrieval provides source attribution and lets access be governed through IAM, avoiding custom result-merging logic. Amazon Bedrock RetrieveAndGenerate returns citations tied to retrieved source references. One implementation detail matters under current AWS documentation: structured Knowledge Bases use Amazon Redshift as the query engine; Amazon Redshift federated queries can access Amazon RDS and Aurora operational databases. Therefore, the RDS-backed knowledge-base leg should be implemented through that supported structured-data path rather than interpreted as a direct native RDS vector connector. Options B, C, and D introduce extra agents or Lambda-based retrieval/merging, which increases development effort and makes unified citation handling more custom. A keeps both information sources behind managed retrieval and preserves provenance while IAM controls access. AWS Documentation

NEW QUESTION # 54
A company is using Amazon Bedrock to design an application to help researchers apply for grants. The application is based on an Amazon Nova Pro foundation model (FM). The application contains four required inputs and must provide responses in a consistent text format. The company wants to receive a notification in Amazon Bedrock if a response contains bullying language. However, the company does not want to block all flagged responses.
The company creates an Amazon Bedrock flow that takes an input prompt and sends it to the Amazon Nova Pro FM. The Amazon Nova Pro FM provides a response.
Which additional steps must the company take to meet these requirements? (Select TWO.)

A. Create an Amazon Bedrock application inference profile that specifies an Amazon Nova Pro FM.Specify the output format for the response in the description. Include a tag for each of the input variables. Add the profile to the prompts node of the flow.
B. Create an Amazon Bedrock guardrail that applies the insults content filter. Set the filter response to detect. Add the guardrail to the prompts node of the flow.
C. Use Amazon Bedrock Prompt Management to specify the required inputs as variables. Select an Amazon Nova Pro FM. Specify the output format for the response. Add the prompt to the prompts node of the flow.
D. Create an Amazon Bedrock guardrail that applies the hate content filter. Set the filter response to block.
Add the guardrail to the prompts node of the flow.
E. Create an Amazon Bedrock prompt router. Specify an Amazon Nova Pro FM. Add the required inputs as variables to the input node of the flow. Add the prompt router to the prompts node. Add the output format to the output node.

Answer: B,C

Explanation:
The correct answers are A and D because they collectively satisfy the requirements for structured inputs, consistent output formatting, and non-blocking detection of bullying language.
Option A is required because Amazon Bedrock Prompt Management enables prompt templates with explicit input variables and defined output formats. By defining the four required inputs as variables, the company ensures that every invocation of the Amazon Nova Pro FM receives the correct structured inputs. Specifying the output format ensures consistent responses, which is essential for a grants application workflow. Adding the managed prompt to the prompts node of the flow allows Bedrock Flows to invoke the model using this standardized configuration.
Option D addresses the requirement to receive notifications when bullying language is detected without blocking responses. Amazon Bedrock guardrails support content filters with configurable actions. By applying the insults content filter and setting the response action to detect, the system flags responses containing bullying or insulting language while still allowing the response to be returned. This enables monitoring, alerting, and auditing without interrupting application functionality.
Option B is incorrect because setting the filter response to block contradicts the requirement not to block all flagged responses. Option C introduces a prompt router, which is unnecessary because the application uses a single Amazon Nova Pro FM. Option E incorrectly attempts to enforce input variables and output formatting through an inference profile, which does not provide prompt-level variable enforcement or formatting guarantees.
Therefore, A and D together provide structured prompt management and non-blocking safety detection with minimal operational complexity.

NEW QUESTION # 55
......

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