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Amazon AIP-C01 Exam Syllabus Topics:

Section Weight Objectives
Topic 1: Implementation and Integration 26% - Integrate with existing systems and services
  • 1. Integration with compute, storage, and networking
  • 2. Identity, security, and access control
- Implement generative AI features and capabilities
  • 1. Prompt engineering and optimization
  • 2. Custom model deployment and fine-tuning
- Integrate foundation models into applications and workflows
  • 1. API integration and workflow orchestration
  • 2. Use AWS services like Amazon Bedrock, SageMaker
Topic 2: Operational Efficiency and Optimization for GenAI Applications 12% - Deploy and manage scalable solutions
  • 1. Monitoring, logging, and observability
  • 2. Infrastructure deployment, automation, and IaC
- Optimize performance and cost
  • 1. Latency, throughput, and resource utilization
  • 2. Cost management and right-sizing
Topic 3: AI Safety, Security, and Governance 20% - Secure generative AI systems
  • 1. Threat modeling and vulnerability management
  • 2. Model security, data protection, and encryption
- Implement safety and guardrails
  • 1. Content filtering, moderation, and risk control
  • 2. Prevent harmful outputs and misuse
- Establish governance frameworks
  • 1. Policies, standards, and audit processes
  • 2. Lifecycle management and change control
Topic 4: Testing, Validation, and Troubleshooting 11% - Troubleshoot and resolve issues
  • 1. Diagnose errors in integration, deployment, or data
  • 2. Debugging and performance tuning
- Validate quality and reliability
  • 1. Output accuracy, relevance, and consistency
  • 2. Functional and non-functional testing
Topic 5: Foundation Model Integration, Data Management, and Compliance 31% - Analyze requirements and design generative AI solutions
  • 1. Architectural design aligned with business goals
  • 2. Select appropriate foundation models and services
- Ensure compliance and responsible AI practices
  • 1. Data privacy, security, and regulatory adherence
  • 2. Bias mitigation, interpretability, and transparency
- Manage data for generative AI applications
  • 1. Vector databases and retrieval-augmented generation (RAG)
  • 2. Data preparation, ingestion, and transformation

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q82-Q87):

NEW QUESTION # 82
A financial services company is building a fraud detection system by using Amazon Bedrock. The system will monitor activity in multiple stock trading applications that run in the United States and Europe. The system must process 1,000 transactions every second with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions.
The company must ensure that data for European customers is processed only in AWS Regions that are based in Europe.
Which solution will meet these requirements?

  • A. Configure all applications to use the InvokeModelWithResponseStream API with on-demand throughput. Deploy an Amazon API Gateway REST API with Regional endpoints in each location where the company operates to route requests to the closest Amazon Bedrock endpoint. Create separate IAM roles for applications that run in the United States and Europe. Grant the IAM roles Region- specific permissions.
  • B. Configure AWS Lambda functions and Amazon EKS applications to use the InvokeModel API with a global inference profile. Deploy an automated failover system that uses Amazon Route 53 health checks. Create a dedicated European inference profile and enable geographic cross-Region inference for European applications. Use Amazon CloudWatch alarms to monitor utilization metrics.
  • C. Configure all applications to use the InvokeModel API with provisioned throughput for an Anthropic Claude model in each Region separately. Set up a custom Application Load Balancer to distribute traffic based on Regional capacity and response times. Implement a Regional failover mechanism that uses Amazon EventBridge rules to handle connectivity disruptions.
  • D. Configure applications that run in the United States to use provisioned throughput with the InvokeModel API. Configure European applications to use a Europe-specific geographic inference profile to ensure data sovereignty. Configure automatic scaling for provisioned capacity based on utilization metrics. Use Amazon EventBridge and AWS Lambda functions to implement cross-Region failover mechanisms.

Answer: D

Explanation:
Option D is correct because it separates the United States and European workloads and uses the correct Amazon Bedrock inference pattern for European data residency. AWS documentation states that geographic cross-Region inference keeps data processing within specified geographic boundaries such as US, EU, and APAC, and is intended for organizations with data residency and compliance requirements. The regional availability documentation further explains that a geographic profile routes within a defined geography and that prompts and outputs may move within that geography but not outside it. This directly satisfies the requirement that European customer data must be processed only in Europe-based AWS Regions.
Provisioned throughput is appropriate for the US workload because the system has a demanding and predictable performance target: 1,000 transactions per second with sub-500 ms response times. Provisioned capacity is generally used when an application needs reserved throughput and consistent performance rather than depending only on on-demand capacity. Automatic scaling based on utilization metrics supports changing demand while maintaining performance. EventBridge and Lambda can coordinate failover workflows or routing updates when connectivity disruptions occur, supporting high availability across Regions.
Option A is not the best answer because it includes a global inference profile. AWS documentation states that global cross-Region inference can route requests across supported commercial AWS Regions worldwide and has no geographic data-residency restriction. AWS explicitly notes that organizations with data residency or compliance requirements should assess whether global cross-Region inference fits their framework because requests may be processed in other supported commercial Regions. That conflicts with the European data processing requirement.
Option B uses separate Regional provisioned throughput, but it relies on a custom Application Load Balancer approach for Bedrock routing and does not use the Bedrock geographic inference profile that is designed for EU data residency. Option C uses response streaming, which improves perceived response latency for text generation but does not guarantee sub-500 ms fraud-processing latency or European-only processing.
Therefore, option D is the most complete AWS-aligned solution.

 

NEW QUESTION # 83
A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models.
The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic.
The company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change.
Which solution will meet these requirements?

  • A. Store model invocation logs in Amazon S3. Use AWS Glue and Amazon Athena to analyze token usage trends.
  • B. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch metric filters to extract tool-specific invocation patterns. Apply CloudWatch anomaly detection alarms that automatically adjust baselines for each tool's token metrics.
  • C. Store model invocation logs in an Amazon S3 bucket. Use AWS Lambda to process logs in real time.Manually update CloudWatch alarm thresholds based on trends identified by the Lambda function.
  • D. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch dashboards for token metrics. Configure static CloudWatch alarms with fixed thresholds for each tool integration.

Answer: B

Explanation:
Option C best meets the requirements by combining native Amazon Bedrock logging with adaptive monitoring and minimal operational overhead. Amazon Bedrock model invocation logging can be sent directly to CloudWatch Logs, where detailed fields such as InputTokenCount, OutputTokenCount, and tool invocation metadata are captured for each request.
CloudWatch metric filters allow extraction of structured metrics from logs, including tool-specific token consumption patterns. By defining filters per tool integration, the company can isolate which tools are responsible for increased token usage without building custom log-processing pipelines.
CloudWatch anomaly detection provides automatic baseline modeling and dynamic thresholds based on historical traffic patterns. Unlike static alarms, anomaly detection adapts as usage evolves, making it ideal for applications with changing workloads or seasonal usage patterns. This directly satisfies the requirement to automatically adjust thresholds as traffic patterns change.
When abnormal token consumption occurs, anomaly detection alarms trigger immediately, enabling rapid investigation and remediation. Because this solution uses fully managed AWS services without custom analytics jobs or manual threshold tuning, it significantly reduces operational effort.
Option A fails to adapt to changing patterns. Option B introduces batch analysis and delayed insights. Option D requires manual intervention and custom code, increasing maintenance burden.
Therefore, Option C provides the most scalable, adaptive, and low-maintenance solution for monitoring and controlling token consumption in Amazon Bedrock-based applications.

 

NEW QUESTION # 84
A company is building a video analysis platform on AWS. The platform will analyze a large video archive by using Amazon Rekognition and Amazon Bedrock. The platform must comply with predefined privacy standards. The platform must also use secure model I/O, control foundation model (FM) access patterns, and provide an audit of who accessed what and when.
Which solution will meet these requirements?

  • A. Define access control by using IAM with attribute-based access control (ABAC) to map departments to specific permissions. Configure VPC endpoints for Amazon Bedrock model API calls. Use IAM condition keys to enforce specific GuardrailIdentifier and ModelId values. Configure AWS CloudTrail to capture management and data events for S3 objects and KMS key usage activities. Enable S3 server access logging to record detailed file-level interactions with the video archives. Send all CloudTrail logs to AWS CloudTrail Lake. Set up Amazon CloudWatch alarms to detect and alert on unexpected activity from Amazon Bedrock, Amazon Rekognition, and AWS KMS.
  • B. Configure AWS CloudTrail Insights to analyze API call patterns across accounts and detect anomalous activity in Amazon Bedrock, Amazon Rekognition, Amazon S3, and AWS KMS. Deploy Amazon Macie to scan and classify the video archive. Use server-side encryption with AWS KMS keys (SSE- KMS) to encrypt all stored data. Configure CloudTrail to capture KMS API usage events for audit purposes. Configure Amazon EventBridge rules to process CloudTrail Insights anomalies and Macie findings. Use CloudWatch alarms to trigger automated notifications and security responses when potential security issues are detected.
  • C. Configure VPC endpoints for Amazon Bedrock model API calls. Implement Amazon Bedrock guardrails to filter harmful or unauthorized content in prompts and responses. Use Amazon Bedrock trace events to track all agent and model invocations for auditing purposes. Export the traces to Amazon CloudWatch Logs as an audit record of model usage. Store all prompts and outputs in Amazon S3 with server-side encryption with AWS KMS keys (SSE-KMS).
  • D. Restrict access to services by using VPC endpoint policies. Use AWS Config to track resource changes and compliance with security rules. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt data at rest. Store the model's I/O in separate Amazon S3 buckets. Enable S3 server access logging to track file-level interactions.

Answer: A

Explanation:
Option B is the correct solution because it delivers end-to-end governance, security, and auditability across Amazon Bedrock, Amazon Rekognition, and the underlying data layer while meeting strict privacy and compliance requirements.
Using IAM attribute-based access control (ABAC) allows the company to control access to foundation models and data based on department, role, or workload attributes rather than static permissions. This is critical for controlling FM access patterns at scale. Enforcing specific ModelId and GuardrailIdentifier values with IAM condition keys ensures that only approved models and guardrails are used, which directly supports secure model I/O and governance requirements.
Configuring VPC endpoints for Amazon Bedrock ensures that all model invocations remain on private AWS network paths, reducing data exfiltration risk and supporting privacy standards. AWS CloudTrail captures both management and data events, providing a definitive audit trail of who accessed which resources and when. Sending logs to CloudTrail Lake enables centralized, long-term, queryable auditing across services.
Amazon S3 server access logging adds file-level visibility into video archive access, which is essential for compliance and forensic analysis. Amazon CloudWatch alarms provide near real-time detection of anomalous or unauthorized activity across Amazon Bedrock, Amazon Rekognition, and AWS KMS.
Option A focuses primarily on model-level tracing but lacks comprehensive IAM governance and S3 access auditing. Option C provides partial controls but lacks identity-aware auditing and model governance. Option D focuses on anomaly detection and classification but does not explicitly control FM access patterns.
Therefore, Option B best satisfies all stated requirements in a unified, auditable, and security-first architecture.

 

NEW QUESTION # 85
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. Increase the number of model units (MUs) in the provisioned throughput configuration.
  • B. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
  • C. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
  • D. Modify the application to use the invokeModelWithResponseStream API instead of the invokeModel API.

Answer: C

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 # 86
A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in a PostgreSQL database.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?

  • A. Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM.
    Perform k-nearest neighbors (k-NN) searches to find semantically similar results.
  • B. Keep the restaurant data in PostgreSQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.
  • C. Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, feature, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
  • D. Migrate the restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline. Configure the knowledge base to automatically generate embeddings from restaurant information. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.

Answer: D

Explanation:
Option D requires the least development effort because it uses a managed retrieval workflow that bundles the most time-consuming parts of semantic search: embedding generation, vector indexing, and natural language retrieval. With an Amazon Bedrock knowledge base, the application does not need to implement and operate separate services to (1) generate embeddings for hundreds of millions of records, (2) store and manage vectors, (3) build query-time embedding conversion logic, and (4) implement k-NN search orchestration.
Instead, the knowledge base is configured to automatically create embeddings during ingestion, and the application queries it using the Amazon Bedrock Retrieve API, which accepts natural language input and performs the vector search as a managed capability.
The performance requirement (95% of queries within 500 ms) is best served by a purpose-built vector search backend rather than running similarity search directly inside a transactional PostgreSQL system at this scale.
A knowledge base is designed for retrieval patterns and can be backed by scalable vector stores, which helps meet latency goals under heavy concurrency. The hourly freshness requirement maps naturally to ingestion updates: the pipeline can re-ingest updated restaurant details on a schedule so the knowledge base remains current without building custom re-embedding workflows in application code.
Cost-effective scaling during peak periods is also easier with a managed retrieval layer because scaling the retrieval workload is separated from the operational database. This avoids overprovisioning PostgreSQL for peak semantic-search traffic and reduces the engineering effort to tune performance, sharding, indexing, and retry logic.
Options B and C can work, but they require the team to build and maintain embedding pipelines, query embedding generation, vector index management, and operational scaling strategies. Option A does not provide semantic search because it relies on keyword-based matching rather than embeddings.

 

NEW QUESTION # 87
......

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