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Amazon AIP-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Plan and Design a Generative AI Application	25%	- 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 - Select the appropriate foundation models (FMs) and techniques 1. Select model parameters and configurations 2. Evaluate foundation models for the use case 3. Consider fine-tuning vs. retrieval-augmented generation (RAG) 4. Design prompt engineering strategies - Identify and define the business and technical requirements for a generative AI application 1. Determine data requirements and availability 2. Identify the target audience and use cases 3. Define functional and non-functional requirements 4. Identify constraints and risks
Optimize and Operationalize a Generative AI Application	30%	- Implement CI/CD and automation for AI applications 1. Automate deployment of AI models and applications 2. Implement version control for prompts and models 3. Implement testing strategies for generative AI applications 4. Manage model updates and rollback strategies - 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 - 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
Build and Implement a Generative AI Application	45%	- Develop the application using AWS AI services 1. Implement prompt engineering and template management 2. Integrate with knowledge bases 3. Implement inference calls to Amazon Bedrock or other FMs 4. Implement retrieval-augmented generation (RAG) patterns - Implement data preprocessing and vectorization pipelines 1. Manage vector stores (e.g., Amazon OpenSearch, Amazon Aurora) 2. Ingest and transform data for model consumption 3. Implement document chunking and text processing 4. Implement embeddings generation using AWS services - Implement security, compliance, and responsible AI 1. Ensure compliance with AI ethics and responsible use 2. Implement data encryption and privacy controls 3. Implement IAM roles and policies for AI services 4. Implement content filtering and safety mechanisms

Section

Weight

Objectives

Plan and Design a Generative AI Application

25%

- 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

- Select the appropriate foundation models (FMs) and techniques

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

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

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

Optimize and Operationalize a Generative AI Application

30%

- Implement CI/CD and automation for AI applications

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

- 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

- 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

Build and Implement a Generative AI Application

45%

- Develop the application using AWS AI services

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

- Implement data preprocessing and vectorization pipelines

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

- Implement security, compliance, and responsible AI

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q149-Q154):

NEW QUESTION # 149
An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations.
The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company's product catalog. The company must resolve these issues.
Which solution will meet this requirement?

A. Store product catalog data in Amazon OpenSearch Service. Validate the model's product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.
B. Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers.
C. Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation RAG. Set the PerformanceConfigLatency parameter to optimized.
D. Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput.

Answer: C

Explanation:
Option C best addresses both core problems: hallucinated recommendations that do not exist in the catalog and slow response times, while keeping operational overhead low. The most direct way to prevent the model from recommending unavailable products is to ground generation on authoritative product catalog data at inference time. An Amazon Bedrock knowledge base is designed for this pattern by ingesting domain data, chunking content, creating embeddings, and retrieving the most relevant catalog entries when a user asks for recommendations. Implementing Retrieval Augmented Generation ensures the foundation model receives only approved, catalog-backed context and can cite or base its output on those retrieved items. This sharply reduces the likelihood of inventing products, because the response is conditioned on retrieved catalog records rather than relying on the model's parametric memory.
The requirement also notes that most interactions are unique. That makes response caching far less effective, because there are fewer repeated prompts to benefit from cached outputs. Instead, improving the retrieval and model invocation path is the better optimization. Using the PerformanceConfigLatency parameter set to optimized prioritizes lower latency behavior for model inference, helping meet faster recommendation generation without requiring the company to build and operate additional infrastructure.
The other options do not solve the root cause as reliably. Prompt engineering and streaming can improve perceived latency, but they do not guarantee catalog-only recommendations because the model can still hallucinate items. Guardrails can help detect or block certain undesired outputs, but without consistent catalog grounding they do not ensure every recommendation is derived from the company's product data. Building a custom OpenSearch validation and caching layer increases operational complexity, and caching is misaligned with predominantly unique interactions.

NEW QUESTION # 150
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

NEW QUESTION # 151
A company is implementing a serverless inference API by using AWS Lambda. The API will dynamically invoke multiple AI models hosted on Amazon Bedrock. The company needs to design a solution that can switch between model providers without modifying or redeploying Lambda code in real time. The design must include safe rollout of configuration changes and validation and rollback capabilities.
Which solution will meet these requirements?

A. Configure an Amazon API Gateway REST API to route requests to separate Lambda functions.
Hardcode each Lambda function to a specific model provider. Switch the integration target manually.
B. Store the active model provider in AWS Systems Manager Parameter Store. Configure a Lambda function to read the parameter at runtime to determine which model to invoke.
C. Store the active model provider in a JSON file hosted on Amazon S3. Use AWS AppConfig to reference the S3 file as a hosted configuration source. Configure a Lambda function to read the file through AppConfig at runtime to determine which model to invoke.
D. Store the active model provider in AWS AppConfig. Configure a Lambda function to read the configuration at runtime to determine which model to invoke.

Answer: D

Explanation:
Option B is the correct solution because AWS AppConfig is specifically designed to support dynamic configuration management with safe rollout, validation, and rollback, which are explicit requirements in the scenario.
By storing the active model provider configuration in AWS AppConfig, the company can switch between Amazon Bedrock model providers in real time without redeploying Lambda code. AppConfig supports deployment strategies such as canary releases, linear rollouts, and immediate deployments, allowing safe and controlled changes. If a configuration causes issues, AppConfig supports automatic rollback, reducing operational risk.
AWS AppConfig also supports schema validation, ensuring that configuration values such as model identifiers, provider names, or inference parameters are valid before being applied. This prevents misconfiguration from impacting production workloads.
Option A uses Parameter Store, which lacks native rollout strategies, validation, and automated rollback, making it unsuitable for safe real-time switching. Option C requires manual routing changes and code coupling, increasing operational overhead and deployment risk. Option D introduces unnecessary complexity by hosting configuration files in Amazon S3 when AppConfig already supports native hosted configurations.
Therefore, Option B provides the most robust, scalable, and low-maintenance solution for dynamic model switching in a serverless Amazon Bedrock inference architecture.

NEW QUESTION # 152
A media company is building an AI-powered content moderation system by using Amazon Bedrock. The system first classifies text by using a small, low-latency model. Then the system escalates requests that have a confidence score below 0.65 to a larger, more expensive model.
The system must respond in near real time for high-confidence results. The system must process low- confidence requests asynchronously. The system must scale to meet sudden spikes in demand. The company wants to optimize costs for the system by invoking the larger model only when required. The company wants to use decoupled components to achieve high resiliency for the system.
Which solution will meet these requirements?

A. Send requests to an Amazon SQS queue. Use AWS Fargate to process messages. Invoke the small model first. If the confidence score is below 0.65, place the request in a second SQS queue to process asynchronously by using the large model.
B. Deploy both models on Amazon EC2 instances and enable auto scaling. Use a custom application heuristic to route requests to the appropriate instance based on phrase length and keyword rules.
C. Use an AWS Step Functions workflow that has parallel branches to run both the small model and the large model for every request. Choose the large model result when confidence score values differ.
D. Use Amazon API Gateway to invoke the small model synchronously. If the small model's confidence score is below 0.65, synchronously call the larger model. Use provisioned concurrency to handle traffic spikes.

Answer: A

Explanation:
Option C is the best answer because it implements a decoupled, queue-based moderation pipeline that invokes the expensive model only when the low-latency model is not confident enough. Amazon SQS is designed to decouple distributed application components and support asynchronous processing. AWS documentation describes SQS as a fully managed message queuing service that enables decoupling and scaling of microservices, distributed systems, and serverless applications. This matches the requirement for high resiliency and sudden demand spikes because incoming requests can be buffered in a durable queue rather than overwhelming the model-processing layer.
Using AWS Fargate to process queue messages is also appropriate because Fargate provides serverless container compute for Amazon ECS or Amazon EKS workloads. It allows the company to run scalable processing workers without managing EC2 capacity directly. AWS Prescriptive Guidance includes architectures that use API Gateway, Amazon SQS, and AWS Fargate to process events asynchronously, which supports the same decoupled processing model required in this question.
The two-stage queue design also optimizes cost. The small, low-latency classifier is used first for all requests.
Only requests with confidence below 0.65 are placed into the second queue and processed by the larger model. This avoids running the larger model for every moderation request. High-confidence results can be completed quickly by the first-stage processor, while uncertain results are isolated into an asynchronous second-stage workflow.
Option A is incorrect because it synchronously calls the larger model for low-confidence results, which violates the requirement to process low-confidence requests asynchronously. Option B is incorrect because it invokes both models for every request, increasing cost and eliminating the benefit of confidence-based escalation. Option D requires managing EC2 instances and uses keyword heuristics instead of model confidence, so it is less resilient and less aligned with Bedrock-based moderation. Therefore, option C is correct.

NEW QUESTION # 153
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. 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.
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. 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.

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 # 154
......

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