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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Build and Implement a Generative AI Application	45%	- Develop the application using AWS AI services 1. Integrate with knowledge bases 2. Implement prompt engineering and template management 3. Implement inference calls to Amazon Bedrock or other FMs 4. Implement retrieval-augmented generation (RAG) patterns - Implement security, compliance, and responsible AI 1. Ensure compliance with AI ethics and responsible use 2. Implement IAM roles and policies for AI services 3. Implement content filtering and safety mechanisms 4. Implement data encryption and privacy controls - Implement data preprocessing and vectorization pipelines 1. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora) 2. Implement document chunking and text processing 3. Implement embeddings generation using AWS services 4. Ingest and transform data for model consumption
Topic 2: Optimize and Operationalize a Generative AI Application	30%	- Optimize costs and performance 1. Optimize model selection and parameter tuning 2. Implement caching strategies for frequent queries 3. Implement auto-scaling for AI workloads 4. Optimize token usage and manage costs - Implement monitoring, logging, and evaluation 1. Implement logging for prompts and responses 2. Implement human-in-the-loop evaluation workflows 3. Evaluate model outputs for quality and bias 4. Monitor model performance and application metrics - Implement CI/CD and automation for AI applications 1. Implement version control for prompts and models 2. Manage model updates and rollback strategies 3. Automate deployment of AI models and applications 4. Implement testing strategies for generative AI applications
Topic 3: Plan and Design a Generative AI Application	25%	- Identify and define the business and technical requirements for a generative AI application 1. Identify constraints and risks 2. Define functional and non-functional requirements 3. Identify the target audience and use cases 4. Determine data requirements and availability - Select the appropriate foundation models (FMs) and techniques 1. Design prompt engineering strategies 2. Consider fine-tuning vs. retrieval-augmented generation (RAG) 3. Evaluate foundation models for the use case 4. Select model parameters and configurations - Design the generative AI solution architecture 1. Integrate with other AWS services (e.g., storage, databases, security) 2. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker) 3. Design for scalability, reliability, and cost-effectiveness 4. Design the end-to-end solution architecture

Section

Weight

Objectives

Topic 1: Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

1. Integrate with knowledge bases
2. Implement prompt engineering and template management
3. Implement inference calls to Amazon Bedrock or other FMs
4. Implement retrieval-augmented generation (RAG) patterns

- Implement security, compliance, and responsible AI

1. Ensure compliance with AI ethics and responsible use
2. Implement IAM roles and policies for AI services
3. Implement content filtering and safety mechanisms
4. Implement data encryption and privacy controls

- Implement data preprocessing and vectorization pipelines

1. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora)
2. Implement document chunking and text processing
3. Implement embeddings generation using AWS services
4. Ingest and transform data for model consumption

Topic 2: Optimize and Operationalize a Generative AI Application

30%

- Optimize costs and performance

1. Optimize model selection and parameter tuning
2. Implement caching strategies for frequent queries
3. Implement auto-scaling for AI workloads
4. Optimize token usage and manage costs

- Implement monitoring, logging, and evaluation

1. Implement logging for prompts and responses
2. Implement human-in-the-loop evaluation workflows
3. Evaluate model outputs for quality and bias
4. Monitor model performance and application metrics

- Implement CI/CD and automation for AI applications

1. Implement version control for prompts and models
2. Manage model updates and rollback strategies
3. Automate deployment of AI models and applications
4. Implement testing strategies for generative AI applications

Topic 3: Plan and Design a Generative AI Application

25%

- Identify and define the business and technical requirements for a generative AI application

1. Identify constraints and risks
2. Define functional and non-functional requirements
3. Identify the target audience and use cases
4. Determine data requirements and availability

- Select the appropriate foundation models (FMs) and techniques

1. Design prompt engineering strategies
2. Consider fine-tuning vs. retrieval-augmented generation (RAG)
3. Evaluate foundation models for the use case
4. Select model parameters and configurations

- Design the generative AI solution architecture

1. Integrate with other AWS services (e.g., storage, databases, security)
2. Select appropriate AWS AI/ML services (e.g., Amazon Bedrock, Amazon SageMaker)
3. Design for scalability, reliability, and cost-effectiveness
4. Design the end-to-end solution architecture

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q80-Q85):

NEW QUESTION # 80
An ecommerce company is using an Anthropic Claude Sonnet model in Amazon Bedrock to generate product recommendations. An AWS Lambda function retrieves customer purchase data from Amazon DynamoDB, product reviews from Amazon S3, and customer profile information from Amazon RDS. Then the function sends the data directly to the Amazon Bedrock model through API calls. Recently, customers who have extensive purchase histories have begun to receive incomplete recommendations.
Amazon CloudWatch logs for the Lambda function show execution timeouts. CloudWatch logs for Amazon Bedrock API calls show intermittent errors. The company reviews the logs and finds that some requests are failing with context-length-exceeded errors. Other requests finish but appear to ignore portions of the input data.
The company wants the recommendation system to consider all customer data when the system generates recommendations. The company wants to use Amazon Bedrock Knowledge Bases to improve data organization and retrieval.
Which combination of solutions will meet these requirements? (Select TWO.)

A. Replace Claude Sonnet with a model that has a larger context-window capacity. Increase the Lambda function timeout to accommodate longer processing times for larger inputs.
B. Implement a chunking strategy that divides the customer data into smaller segments. Configure the model to process each segment separately. Invoke the model a final time to synthesize the individual responses into comprehensive recommendations.
C. Modify the prompt structure to place the most critical information at the beginning and end of the context window. Implement token-counting logic to truncate less important data when the interaction approaches the model's maximum context length.
D. Implement RAG by using a knowledge base to index the customer data with vector embeddings.Retrieve only the most semantically relevant information for each recommendation request based on the current customer context.
E. Configure the recommendation system to use the Converse API. Modify the additionalModelRequestFields parameter to increase the maximum token limit beyond the model's default context-window size.

Answer: B,D

Explanation:
Options A and E are correct because the failure pattern is caused by exceeding the model's usable context window and by sending too much raw customer data directly to the FM. Amazon Bedrock Knowledge Bases follows the RAG pattern by splitting source content into manageable chunks, converting chunks into embeddings, storing them in a vector index, and using semantic similarity to retrieve relevant content for a user query. This supports option E because the application can index customer purchases, profiles, and review- related information, then retrieve only the most relevant records for the current recommendation request instead of stuffing all raw data into one prompt.
Option A is also correct because chunking and staged synthesis are standard ways to handle inputs that exceed an FM's context capacity. Processing smaller segments avoids context-length-exceeded errors, and a final synthesis call can combine intermediate findings into a complete recommendation. This is especially useful when the company says it wants the system to consider all customer data, not merely truncate older or less important data. Amazon Bedrock Knowledge Bases also lets teams configure how content is chunked for storage and retrieval, which supports a structured approach rather than an oversized one-shot prompt.
Option B is not sufficient because truncating "less important" data conflicts with the requirement to consider all customer data. Option C violates the implied design direction because the company wants to use Knowledge Bases, and simply choosing a larger context model does not solve long-term growth of customer histories. Option D is technically incorrect: model parameters such as max_tokens control output generation limits, not the model's maximum context window, and different Claude models have model-specific maximum values. You cannot use additionalModelRequestFields to bypass the FM's context limit.

NEW QUESTION # 81
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. Deploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply Amazon Bedrock guardrails that have semantic denial rules to block unsafe outputs. Use Amazon Bedrock Prompt Management to manage prompts by using approval workflows.
B. 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.
C. 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.
D. Use Amazon Bedrock Agents to manage chaining. Log model inputs and outputs to Amazon CloudWatch Logs. Use logs from Amazon CloudWatch to perform A/B testing for prompt versions.

Answer: A

Explanation:
Option A best meets the combined requirements of low latency, stability, and validated safety controls by using purpose-built Amazon Bedrock features designed for production GenAI operations. The company's latency target of under 1 second and its observation of degradation during spikes strongly indicate capacity and throughput variability. Provisioned throughput for Amazon Bedrock is intended to deliver more predictable performance by reserving inference capacity for a chosen model, reducing throttling risk and stabilizing response times under load. This directly improves operational consistency across Regions where on-demand capacity can vary.
The requirement to "block unsafe or hallucinated recommendations" is most directly addressed by Amazon Bedrock Guardrails. Guardrails provide managed safety enforcement, including sensitive information controls and configurable content policies. Using semantic denial rules enables the application to prevent unsafe guidance such as dangerous brewing temperatures or other harmful procedural instructions, enforcing safety at the model boundary rather than relying on downstream filtering.
The remaining requirement is "99.5% output consistency for identical inputs." While generative models can be probabilistic, production systems achieve practical consistency by controlling prompt versions, inputs, and policy behavior. Amazon Bedrock Prompt Management supports controlled prompt lifecycle practices, including versioning and approval workflows, which reduce unintended drift across deployments and Regions. By ensuring the same approved prompt templates and parameters are used consistently, the company can materially improve repeatability for the same structured inputs and retrieval context, which is essential in multi-stage prompt chains.
The other options are incomplete. B improves experimentation and observability but does not enforce safety controls or stabilize latency. C can improve performance, but it does not provide validated safety enforcement at inference time. D can help retrieval relevance, but it does not address unsafe outputs or inference stability.
Therefore, A is the only option that simultaneously targets predictable latency, governance of prompt behavior, and strong safety controls within Amazon Bedrock.

NEW QUESTION # 82
A publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM.
Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant.
Which combination of solutions will meet this requirement? (Select TWO.)

A. Enable model preload upon container startup. Implement dynamic batching to process multiple user requests together in a single inference pass.
B. Select a larger GPU instance type for the SageMaker AI endpoint. Set the minimum number of instances to 0. Continue to perform per-request processing. Lazily load model weights on the first request.
C. Switch to a multi-model endpoint. Use lazy loading without request batching.
D. Set the minimum number of instances to greater than 0. Enable response streaming.
E. Switch to Amazon SageMaker Asynchronous Inference for all requests. Store requests in an Amazon S3 bucket. Set the minimum number of instances to 0.

Answer: A,D

NEW QUESTION # 83
A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data from the documents. The application must redact personally identifiable information (PII) before inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant.
The company must ensure that the application complies with strict Regional data residency and auditability requirements. The application must be able to scale to handle 25,000 applications each day and provide 99.9% availability.
Which combination of solutions will meet these requirements? (Select THREE.)

A. Use AWS Lambda functions to detect and redact PII from submitted documents before inference.
Apply Amazon Bedrock guardrails to prevent inappropriate or unauthorized content in model outputs.
Configure Region-specific IAM roles to enforce data residency requirements and to control access to the extracted data.
B. Use AWS Glue Data Quality to validate the structured document data. Use AWS Step Functions to orchestrate a review workflow that includes a prompt engineering step that transforms validated data into optimized prompts before invoking Amazon Bedrock to assess loan applications.
C. Use Amazon Kendra and Amazon OpenSearch Service to extract field-level values semantically from the uploaded documents before inference.
D. Use Amazon SageMaker Clarify to generate fairness and bias reports based on model scoring decisions that Amazon Bedrock makes.
E. Store uploaded documents in Amazon S3 and apply object metadata. Configure IAM policies to store original documents within the same Region as each applicant. Enable object tagging for future audits.
F. Deploy Amazon Textract and Amazon Augmented AI within the same Region to extract relevant data from the scanned documents. Route low-confidence pages to human reviewers.

Answer: A,E,F

Explanation:
The correct combination is A, B, and D because these three options collectively satisfy the mandatory requirements for structured extraction, PII redaction before inference, regional human review, data residency, auditability, and high-scale availability with managed AWS services.
Option A is essential because Amazon Textract is the AWS-managed service designed to extract structured data from scanned documents such as forms, tables, and financial statements. Textract provides confidence scores, and Amazon Augmented AI (A2I) is purpose-built to route low-confidence extractions to human reviewers. Deploying Textract and A2I within the same Region ensures that the human review loop remains regionally constrained, meeting strict data residency requirements for applicants.
Option B satisfies the requirement to redact PII before inference by using AWS Lambda preprocessing. It also adds Amazon Bedrock guardrails to enforce safety controls on model outputs. Region-specific IAM roles ensure that only authorized principals in the correct Region can access the extracted data and invoke downstream services, strengthening residency enforcement and auditability.
Option D ensures that source documents are stored in Amazon S3 in the same Region as the applicant. Object metadata and tagging provide an auditable trail, supporting compliance reporting and traceability. S3 also provides the durability and availability needed to support 99.9% application availability as part of a well- architected pipeline.
Option C is not the correct approach for structured extraction from scans. Option E adds useful quality validation but is not strictly required to meet the stated requirements compared to A, B, and D. Option F is unrelated to the extraction/redaction/residency workflow requirements.
Therefore, A, B, and D are the best three choices to meet all stated requirements with minimal operational overhead.

NEW QUESTION # 84
A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling.
A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company's approved resources. The company does not want to make project-level modifications.
Which solution will meet these requirements?

A. Create a folder in the application project named rules. Store the guidelines and code in the folder for Amazon Q Developer to reference for code suggestions.
B. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.
C. Create a Git repository that contains all of the approved internal libraries, algorithms, and code samples.
Include this Git repository in the application project locally as part of the workspace. Ensure that the developers use the workspace context to retrieve suggestions from the Git repository.
D. In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.

Answer: B

Explanation:
Option D is the correct solution because Amazon Q Developer customizations are designed to incorporate organization-approved knowledge and coding guidance without requiring per-project changes. A customization can point Amazon Q Developer to curated internal sources such as approved libraries, coding standards, architectural patterns, and proprietary techniques. This allows the assistant's suggestions to align with company policies and preferred implementations consistently across teams and repositories.
The key requirement is that the company does not want to make project-level modifications. Options A, B, and C all require adding files or repositories into the project workspace, which directly violates this constraint.
They also rely on developer behavior to "use workspace context," which is harder to enforce and can lead to inconsistent adherence to standards.
With a customization, the organization centrally manages and updates approved resources. This reduces operational overhead because updates to libraries, patterns, or guidelines propagate automatically to developers using the customization, without requiring changes to each project. This is especially valuable for a new team, where consistent enforcement of approved practices is important to reduce compliance risk, security issues, and inconsistent code style.
Additionally, customizations support governance by allowing the company to standardize how Amazon Q Developer responds, ensuring that suggestions reflect approved internal content rather than generic public patterns.
Therefore, Option D best satisfies the requirement for centralized enforcement of approved resources with minimal ongoing management and no project-level modifications.

NEW QUESTION # 85
......

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