Actual AIP-C01 Test - Exam AIP-C01 Syllabus

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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	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 4	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 5	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.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q104-Q109):

NEW QUESTION # 104
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. Reinforce the requirement to include the new promotional message within product recommendations by using an output indicator in prompts to the FM.
B. Use an Amazon Bedrock Guardrails filter on the prompt. Set the input filter strength to HIGH.
C. 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.
D. 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.

Answer: A

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 # 105
A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located.
The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers.
Which solution will meet these requirements?

A. Configure an Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to mask mode during development and testing. Switch to block mode for production deployment. Deploy a copy of the guardrail to each Region where the company operates.
B. Configure an Amazon Bedrock guardrail to apply content and topic filters. Set the guardrail to detect mode during development, testing, and production. Disable invocation logging for the Amazon Bedrock model.
C. Configure a cross-Region Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to detect mode during development and testing. Switch to block mode for production deployment.
D. Configure a cross-Region Amazon Bedrock guardrail to apply a set of content and word filters. Set the guardrail to detect mode during development and testing. Switch to mask mode for production deployment.

Answer: A

Explanation:
Option B best meets all stated requirements by correctly combining PII protection, evaluation before launch
, and data residency compliance using Amazon Bedrock Guardrails. Amazon Bedrock guardrails provide native sensitive information filtering that operates inline during model invocation, making them well suited for preventing personal data exposure in customer-facing AI assistants.
The requirement to evaluate how effective the AI assistant is at preventing exposure before release is best addressed by using mask mode during development and testing. Mask mode allows responses to be generated while automatically redacting detected personal information, making it easy for developers and reviewers to see where and how PII would have appeared. This provides concrete validation that the guardrail rules are correctly configured without fully blocking responses, which is ideal for quality assurance and pre- production evaluation.
For production, switching the guardrail to block mode ensures that responses containing personal information are fully prevented from being returned to users. This offers the strongest protection and aligns with compliance expectations for customer account data. Block mode is appropriate once confidence in the guardrail configuration has been established during testing.
The data residency requirement is addressed by deploying a copy of the guardrail in each AWS Region where the application operates. Amazon Bedrock guardrails are Region-specific resources, and using Region- local guardrails ensures that inference, filtering, and enforcement all occur within the same Region as the customer data. This avoids cross-Region processing and helps the company comply with regulatory and contractual data residency policies.
Option A and D incorrectly rely on cross-Region guardrails, which can violate data residency constraints.
Option C focuses on topic filtering rather than sensitive information filtering and keeps detect mode enabled in production, which does not actively prevent PII exposure. Therefore, B is the only option that fully satisfies safety, compliance, and evaluation requirements.

NEW QUESTION # 106
A company uses Amazon Bedrock to generate technical content for customers. The company has recently experienced a surge in hallucinated outputs when the company's model generates summaries of long technical documents. The model outputs include inaccurate or fabricated details. The company's current solution uses a large foundation model (FM) with a basic one-shot prompt that includes the full document in a single input.
The company needs a solution that will reduce hallucinations and meet factual accuracy goals. The solution must process more than 1,000 documents each hour and deliver summaries within 3 seconds for each document.
Which combination of solutions will meet these requirements? (Select TWO.)

A. Increase the temperature parameter in Amazon Bedrock.
B. Configure Amazon Bedrock guardrails to block any generated output that matches patterns that are associated with hallucinated content.
C. Implement zero-shot chain-of-thought (CoT) instructions that require step-by-step reasoning with explicit fact verification before the model generates each summary.
D. Use Retrieval Augmented Generation (RAG) with an Amazon Bedrock knowledge base. Apply semantic chunking and tuned embeddings to ground summaries in source content.
E. Prompt the Amazon Bedrock model to summarize each full document in one pass.

Answer: B,D

Explanation:
The correct answers are B and C because they directly address hallucination reduction while maintaining high throughput and low latency.
Option B reduces hallucinations at their source by grounding model outputs in verified content through Retrieval Augmented Generation (RAG). Using an Amazon Bedrock knowledge base with semantic chunking ensures that long technical documents are broken into meaningfully coherent sections. This allows the model to retrieve only the most relevant chunks, rather than processing an entire document in one pass, which significantly improves factual accuracy and reduces cognitive overload on the model. This approach scales efficiently and supports processing more than 1,000 documents per hour.
Option C adds a defense-in-depth safety layer by using Amazon Bedrock guardrails to detect and block hallucination-like output patterns. Guardrails operate at inference time with minimal performance overhead, making them suitable for low-latency requirements. While guardrails do not eliminate hallucinations entirely, they effectively prevent unsafe or clearly fabricated outputs from reaching users.
Option A increases latency and cost due to explicit reasoning steps and does not scale well for high- throughput workloads. Option D increases randomness and worsens hallucinations. Option E repeats the existing flawed approach.
Therefore, Options B and C together provide scalable grounding and runtime protection that meet accuracy, performance, and throughput requirements.

NEW QUESTION # 107
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 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.
B. 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.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 108
A company is creating a workflow to review customer-facing communications before the company sends the communications. The company uses a pre-defined message template to generate the communications and stores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion from the template and send it to an Amazon Bedrock model. The workflow must store model responses back to the original S3 bucket.
Which solution will meet these requirements?

A. Create an Amazon Bedrock agent that has an action group. Configure instructions to define how the agent should parse the communications. Configure the action group to retrieve the communications from the S3 bucket, invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.
B. Create an Amazon Bedrock agent that has a single action group. Configure three AWS Lambda functions in the action group. Configure the functions to retrieve the communications from the S3 bucket, parse the communications and invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.
C. Create an AWS Step Functions Express workflow state machine. Use an Amazon S3 integration GetObject step to retrieve the original communications. Use an intrinsic function Pass step to parse the communications and to pass the results to an Amazon Bedrock InvokeModel step. Configure an Amazon S3 integration PutObject step to store the model responses back to the S3 bucket.
D. Create a flow in Amazon Bedrock Flows. Configure S3 action nodes at the beginning and end of the flow to retrieve and store the communications and the model responses. In the middle of the flow, configure an expression to parse each communication. Configure an agent step to send the parsed input to the model for review.

Answer: D

Explanation:
Option A is the correct answer because Amazon Bedrock Flows is purpose-built to orchestrate generative AI workflows that combine data access, deterministic transformations, and model invocation with minimal operational overhead. The requirements explicitly state that the workflow must retrieve content from Amazon S3, extract a specific portion of a predefined template, send that portion to an Amazon Bedrock model, and store the model's response back into the same S3 bucket. Amazon Bedrock Flows natively supports all of these steps.
By configuring S3 action nodes at the beginning and end of the flow, the workflow can retrieve the original communications and persist the reviewed output without custom code. The expression step allows deterministic parsing of a specific portion of the template, which is essential when only part of the message should be reviewed. This avoids relying on generative logic for parsing, which would be less predictable and harder to audit. The agent step is then used specifically for the review task, where the foundation model evaluates or modifies the extracted content.
Option B uses AWS Step Functions, which can achieve similar outcomes but requires more explicit orchestration logic and does not provide GenAI-native constructs such as expressions and agent steps in a single managed experience. Options C and D rely on Amazon Bedrock agents and AWS Lambda functions to handle parsing and data movement, which increases complexity, operational overhead, and maintenance burden.
Because Amazon Bedrock Flows directly integrates S3 actions, parsing expressions, and model review steps in a single managed workflow, Option A best meets the requirements with the least development and operational effort.

NEW QUESTION # 109
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