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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

Optimize and Operationalize a Generative AI Application

30%

- Implement monitoring, logging, and evaluation

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

- Implement CI/CD and automation for AI applications

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

- Optimize costs and performance

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

Plan and Design a Generative AI Application

25%

- Design the generative AI solution architecture

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

- Select the appropriate foundation models (FMs) and techniques

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

- 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. Determine data requirements and availability
4. Identify the target audience and use cases

Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

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

- Implement security, compliance, and responsible AI

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

- Implement data preprocessing and vectorization pipelines

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q91-Q96):

NEW QUESTION # 91
A company uses Amazon Bedrock to deploy an application that generates technical documentation for users across multiple AWS Regions and in multiple languages. Users frequently submit semantically similar questions in different languages, which results in increased inference costs and response latency.
The company needs a caching solution that significantly reduces inference costs, provides low-latency responses globally, maintains cache freshness with a 5-minute TTL, and minimizes custom cache key generation and application-managed caching logic.
Which solution will meet these requirements?

A. Configure prompt caching in Amazon Bedrock for semantically similar queries across languages. Use Amazon CloudFront and Lambda@Edge functions to handle Regional cache distribution. Set a TTL of
5 minutes for both caching layers.
B. Create Amazon ElastiCache (Redis OSS) clusters in each Region where the application runs to store inference results with custom fingerprinting for multilingual queries. Configure automatic replication between Regional clusters with a 5-minute TTL.
C. Use Amazon DynamoDB Accelerator (DAX) to cache inference results and to automatically manage TTL. Use Amazon CloudFront to distribute API responses globally. Use edge functions to handle language-specific transformations.
D. Create a custom caching system that uses AWS Lambda functions to store inference results in an Amazon DynamoDB table. Use Amazon CloudFront to distribute cached responses to global users with a 5-minute TTL.

Answer: B

Explanation:
Option C is the technically supportable choice under current AWS documentation. Amazon Bedrock prompt caching does not provide semantic caching for differently worded or cross-language questions; it reuses cached prompt content through matching prompt prefixes or defined cache checkpoints. Therefore, Option B ' s assumption that Bedrock prompt caching automatically recognizes semantically equivalent multilingual queries is incorrect, even though many supported models provide a 5-minute prompt-cache TTL. ElastiCache for Redis OSS supports application-controlled cache entries and TTLs, while Global Datastore provides managed cross-Region replication and low-latency geographic reads. A multilingual semantic fingerprint can normalize equivalent requests to a shared cache key before inference. Options A and D similarly lack built-in semantic equivalence handling, while DAX is specifically a DynamoDB caching layer. Thus, C is the viable architecture among the listed choices.

NEW QUESTION # 92
An ecommerce company is developing a generative AI (GenAI) solution that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale or are not relevant. Customers also report long response times for some recommendations.
The company confirms that most customer interactions are unique and that the solution recommends products not present in the product catalog.
Which solution will meet this requirement?

A. Create an Amazon Bedrock Knowledge Bases and implement Retrieval Augmented Generation (RAG).
Set the PerformanceConfigLatency parameter to optimized.
B. Increase grounding within Amazon Bedrock Guardrails. Enable automated reasoning checks. Set up provisioned throughput.
C. Store product catalog data in Amazon OpenSearch Service. Validate model recommendations against the catalog. Use Amazon DynamoDB for response caching.
D. Use prompt engineering to restrict model responses to relevant products. Use streaming inference to reduce perceived latency.

Answer: A

Explanation:
Option C is the correct solution because it directly addresses both correctness and performance issues by grounding the model's responses in authoritative product data using Retrieval Augmented Generation.
Amazon Bedrock Knowledge Bases are designed to connect foundation models to trusted enterprise data sources, ensuring that generated responses are constrained to known, validated content.
By ingesting the product catalog into a knowledge base, the GenAI application retrieves only products that actually exist in the catalog. This prevents hallucinated or unavailable recommendations, which is a common issue when models rely solely on prompt instructions without retrieval grounding. RAG ensures that the model's output is based on retrieved facts rather than learned generalizations.
Setting the PerformanceConfigLatency parameter to optimized enables Bedrock to prioritize lower-latency retrieval and inference paths, improving responsiveness for real-time recommendation scenarios. This directly addresses the reported performance issues without requiring provisioned throughput or caching strategies that are ineffective for mostly unique interactions.
Option A improves safety and latency predictability but does not ensure recommendations are limited to valid products. Option B relies on prompt constraints, which are not sufficient to prevent hallucinations. Option D introduces additional validation and caching layers but increases complexity and does not improve generation relevance.
Therefore, Option C best resolves both relevance and latency challenges using AWS-native, low-maintenance GenAI integration patterns.

NEW QUESTION # 93
An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians.
The company uses Amazon Kinesis Data Streams to collect the elevator sensor data.
New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes.
Which solution will meet these requirements?

A. Create an AWS Glue workflow that has a human approval step. After the human technician review, integrate the application with an AWS Lambda function that calls the SendTaskSuccess API. Store all human technician review decisions in Amazon DynamoDB.
B. Create an AWS Step Functions workflow that has a human approval step that uses the waitForTaskToken API to pause execution. After a human technician completes a review, use an AWS Lambda function to call the SendTaskSuccess API with the approval decision. Store all review decisions in Amazon DynamoDB.
C. Create a custom approval workflow by using AWS Lambda functions and Amazon SQS queues for human review of AI recommendations. Store all review decisions in Amazon DynamoDB for audit purposes.
D. Configure Amazon EventBridge rules with custom event patterns to route AI recommendations to human technicians for review. Create AWS Glue jobs to process human technician approval queues.Use Amazon ElastiCache to cache all human technician review decisions.

Answer: B

Explanation:
AWS Step Functions provides native support for human-in-the-loop workflows, making it the best fit for regulatory oversight requirements. The waitForTaskToken integration pattern is explicitly designed to pause a workflow until an external actor-such as a human reviewer-completes a task.
In this architecture, AI-generated recommendations are sent to a human technician for review. The workflow pauses execution using a task token. Once the technician approves or rejects the recommendation, an AWS Lambda function calls SendTaskSuccess or SendTaskFailure, allowing the workflow to continue deterministically.
This approach ensures full auditability, as Step Functions records every state transition, timestamp, and execution path. Storing review outcomes in Amazon DynamoDB provides durable, queryable audit records required for regulatory compliance.
Option A requires custom orchestration and lacks native workflow state management. Option C incorrectly uses AWS Glue, which is not designed for approval workflows. Option D uses caching instead of durable audit storage and introduces unnecessary complexity.
Therefore, Option B is the AWS-recommended, lowest-risk, and most auditable solution for mandatory human review of AI outputs.

NEW QUESTION # 94
A healthcare company is developing a generative AI (GenAI) application that recommends patient treatment plans to physicians. The company wants to use Amazon Bedrock to build the application.
The application must document model limitations, prevent unauthorized clinical recommendations, and maintain detailed audit trails of all AI-generated outputs. The company must store outputs in compliance with healthcare regulations. The solution must prevent output tampering.
Which solution will meet these requirements?

A. Use model cards to document FM limitations. Implement Amazon Bedrock Guardrails with healthcare compliance policies. Store all AI-generated outputs in Amazon S3. Enable S3 Object Lock.
B. Use model cards to document FM limitations. Implement Amazon Bedrock Guardrails to filter all AI- generated outputs against healthcare regulations. Store the outputs in Amazon S3. Use Amazon QuickSight dashboards to analyze compliance metrics.
C. Use Amazon CloudWatch to monitor and log AI-generated outputs. Configure a postprocessing AWS Lambda function to scan outputs for compliance violations. Store the outputs in Amazon S3. Enable S3 Object Lock.
D. Implement Amazon Bedrock with custom prompt templates that include compliance instructions. Use Amazon DynamoDB to store all AI-generated outputs. Create Amazon CloudWatch alarms that trigger when potential noncompliant outputs are detected.

Answer: A

Explanation:
Option A addresses all three governance layers. Model cards provide documented information about a model' s capabilities, limitations, supported modalities, context constraints, and other characteristics that teams can review before deployment. Amazon Bedrock Guardrails can enforce application-specific safeguards on user inputs and model responses, and IAM policies can require a specific guardrail for inference calls, helping prevent uncontrolled model use. Storing generated outputs in Amazon S3 provides durable audit storage, while S3 Object Lock uses a write-once-read-many model that can prevent retained objects from being overwritten or deleted and is specifically intended to support regulatory retention requirements. Option B lacks formal model-limit documentation and relies on custom postprocessing. Option C does not protect stored audit evidence against tampering. Option D relies primarily on prompts and ordinary database storage rather than enforceable safety and immutable-retention controls. AWS Documentation

NEW QUESTION # 95
A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput.
The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application:
response = bedrock_runtime.invoke_model(
modelId="anthropic.claude-v2",
body=json.dumps(payload)
)
The company needs the application to use the provisioned throughput and to resolve the throttling issues.
Which solution will meet these requirements?

A. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
B. Modify the application to use the invokeModelWithResponseStream API instead of the invokeModel API.
C. Increase the number of model units (MUs) in the provisioned throughput configuration.
D. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.

Answer: D

Explanation:
Option B is the correct solution because Amazon Bedrock provisioned throughput is only used when the application explicitly invokes the provisioned model ARN, not the base foundation model ID. In the provided code, the application is calling the standard model identifier (anthropic.claude-v2), which routes requests to on-demand capacity instead of the purchased provisioned throughput.
When the CreateProvisionedModelThroughput API is used, Amazon Bedrock returns a provisioned model ARN that represents the reserved capacity. Applications must reference this ARN in the modelId parameter when invoking the model. If the base model ID is used instead, Bedrock treats the request as on-demand traffic, which explains why CloudWatch metrics show unused provisioned capacity alongside throttled on- demand requests.
Option A would increase capacity but would not fix the root cause because the application is not using the provisioned resource at all. Option C adds resiliency but does not ensure usage of provisioned throughput and would still incur throttling. Option D changes the response delivery mechanism but does not affect capacity routing.
Therefore, Option B directly resolves the throttling issue by correctly routing traffic to the reserved capacity and ensures that the company benefits from the provisioned throughput it has purchased.

NEW QUESTION # 96
......

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