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Amazon AIP-C01 Exam Syllabus Topics:

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

Section

Weight

Objectives

Plan and Design a Generative AI Application

25%

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

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

- Select the appropriate foundation models (FMs) and techniques

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

- Design the generative AI solution architecture

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

Build and Implement a Generative AI Application

45%

- Implement data preprocessing and vectorization pipelines

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

- Develop the application using AWS AI services

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

- Implement security, compliance, and responsible AI

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

Optimize and Operationalize a Generative AI Application

30%

- Optimize costs and performance

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

- Implement CI/CD and automation for AI applications

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

- Implement monitoring, logging, and evaluation

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

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q133-Q138):

NEW QUESTION # 133
A financial services company wants to use Amazon Bedrock foundation models (FMs) to analyze call center recordings. When calls end, the call center stores recordings as MP3 files in an Amazon S3 bucket. The company needs to generate summaries and sentiment analysis for the recordings in a structured format as soon as new files are created. The recordings average 20 MB in size. Which combination of solutions will meet these requirements? (Select TWO.)

A. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to directly invoke Amazon Bedrock FMs to generate summaries and sentiment analysis in JSON format.
B. Configure the source S3 bucket to send events to Amazon EventBridge. Create an EventBridge rule to invoke the Step Functions workflow when an object is created in the bucket.
C. Configure the source S3 bucket to send notifications to the Step Functions workflow when an object is created in the bucket.
D. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to process the text by using Amazon Bedrock FMs to generate structured analysis output.
E. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to create a prompt to invoke Amazon Bedrock FMs to generate structured analysis output.

Answer: A,B

Explanation:
AWS Step Functions provides native service integrations that minimize code and operational overhead. For this workflow, Step Functions can directly invoke Amazon Transcribe and, upon completion, directly invoke Amazon Bedrock foundation models. Modern foundation models can be prompted to return outputs in JSON format , fulfilling the requirement for structured analysis without needing an intermediate Lambda function to format the request or response. To trigger the process " as soon as new files are created, " configuring Amazon S3 to send events to Amazon EventBridge is the recommended event-driven pattern. This allows for fine-grained routing and decouples the storage layer from the processing logic, ensuring the workflow scales reliably as call volume grows.

NEW QUESTION # 134
A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company ' s data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.
The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application ' s decision-making processes and provide data classification capabilities.
Which solution will meet these requirements?

A. Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring to track model performance and compliance with data residency requirements. Create manual reports for each regulatory jurisdiction.
B. Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit the decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region.
C. Deploy the application in each Region with local IAM policies. Use Amazon Bedrock cross-Region inference to distribute the workload. Use Amazon CloudWatch to log AI decision-making processes and data processing activities. Manually track compliance certifications across Regions.
D. Use Amazon S3 Object Lock with Region-specific S3 bucket policies. Pre-process the data points within the Region based on geographic origin before sending the data points to Amazon Bedrock. Use Amazon Macie to classify the data. Use AWS CloudTrail immutable logs to audit the decision-making processes.

Answer: D

Explanation:
Option C provides the strongest combination of residency controls, data classification, retention protection, and auditability. Keeping source data in Region-specific S3 buckets and processing it according to geographic origin establishes a clear architectural boundary around data belonging to Europe, North America, and Asia.
Amazon S3 Object Lock provides write-once-read-many protection and can prevent protected object versions from being overwritten or deleted, which is particularly valuable for regulated records and audit evidence.
Compliance mode provides especially strong retention enforcement.
Amazon Macie provides the required classification capability. Macie performs automated sensitive-data discovery against Amazon S3 and produces classification results describing whether analyzed objects contain sensitive information. It also calculates sensitivity scores and labels for monitored S3 buckets, supporting governance and compliance workflows.
AWS CloudTrail supplies auditable records of AWS API activity. When CloudTrail log-file integrity validation is enabled, CloudTrail uses SHA-256 hashing and digital signatures so modifications or deletions of delivered logs can be detected. Combined with protected S3 storage, this provides a strong evidentiary audit trail.
AWS now also supports geographic cross-Region inference profiles for Amazon Bedrock that keep inference processing within defined geographic boundaries such as the US, EU, and APAC. However, option A merely states "cross-Region inference" and lacks the required classification service while relying on manual compliance tracking. Geographic residency must be explicitly controlled rather than assumed.
B does not provide a data-classification mechanism. D creates significantly greater administrative burden and depends on custom monitoring and manual reporting. Therefore, C best satisfies the combined residency, classification, audit, and compliance requirements.

NEW QUESTION # 135
A company uses an organization in AWS Organizations with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock across multiple accounts. The company must prevent specific topics and proprietary information from being included in prompts to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company wants to manage these controls centrally.
Which combination of solutions will meet these requirements? (Select TWO.)

A. Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a block filtering policy. Use stack sets to deploy the guardrail to each account in the organization.
B. Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a mask filtering policy. Use stack sets to deploy the guardrail to each account in the organization.
C. Create an IAM permissions boundary for each employee's IAM role. Configure the permissions boundary to require an approved Amazon Bedrock guardrail identifier to invoke Amazon Bedrock models. Create an SCP that allows employees to use only approved models.
D. Create an SCP that prevents an employee from invoking a model if a centrally deployed guardrail identifier is not specified in a call to the model. Create a permissions boundary on each employee's IAM role that allows each employee to invoke only approved models.
E. Create an SCP that allows employees to use only approved models. Configure the SCP to require employees to specify a guardrail identifier in calls to invoke an approved model.

Answer: A,D

Explanation:
The correct combination is C and D because together they enforce centralized governance over both model access and prompt content controls, which are the two core requirements of the scenario.
To ensure employees can use only approved Amazon Bedrock models, governance must be enforced at the organization level and not rely on individual application logic. Service Control Policies (SCPs) are the strongest control mechanism available in AWS Organizations because they define the maximum permissions an account or principal can have. In option C, the SCP prevents any Amazon Bedrock model invocation unless a centrally approved guardrail identifier is specified. This ensures that guardrails are always enforced, regardless of how or where the invocation originates. The additional use of IAM permissions boundaries ensures that even within allowed accounts, employees are restricted to invoking only explicitly approved foundation models.
To prevent specific topics and proprietary information from being included in prompts, Amazon Bedrock Guardrails must be used. Guardrails operate inline during model invocation and can block disallowed content before it is processed by the model. Option D correctly specifies a block filtering policy, which is appropriate when content must be prevented entirely rather than partially redacted. Deploying the guardrail using AWS CloudFormation StackSets allows the company to centrally manage and consistently deploy the same guardrail configuration across all accounts in the organization, ensuring uniform enforcement.
Option E uses mask filtering, which is better suited for redacting sensitive output rather than preventing prohibited content from being submitted in prompts. Option B attempts to use SCPs alone but does not enforce guardrail deployment or content filtering. Option A incorrectly places guardrail enforcement in permissions boundaries, which are not designed to validate request parameters such as guardrail identifiers.
By combining SCP-based enforcement with centrally deployed Bedrock guardrails, options C and D together provide strong, scalable, and centrally managed controls for both content safety and model governance across the organization.

NEW QUESTION # 136
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?

A. Create a SAML identity provider (IdP) in IAM to authenticate by using Microsoft Entra ID. Use IAM permissions boundaries to limit department roles' access to specific model families. Configure public Amazon Bedrock API endpoints with VPC routing to maintain private network connectivity. Set up CloudTrail with Amazon S3 Lifecycle rules to manage audit logs of model interactions.
B. Create an identity provider (IdP) connection in IAM to authenticate by using Microsoft Entra ID.
Assign department permission sets to control access to specific model families. Deploy AWS Lambda functions in private subnets with a NAT gateway for egress to Amazon Bedrock public endpoints.
Enable CloudWatch Logs to capture model interactions for auditing purposes.
C. Configure OpenID Connect (OIDC) federation between Microsoft Entra ID and IAM. Use attribute- based access control to map department attributes to specific model access permissions. Apply SCP policies to restrict access to Amazon Bedrock FM families based on department. Use Microsoft Entra ID's built-in logging capabilities to maintain an audit trail of model interactions.
D. Configure SAML federation between Microsoft Entra ID and AWS Identity and Access Management.
Create department-specific IAM roles that allow only the required ModelId values. Create AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services. Enable AWS CloudTrail to capture Amazon Bedrock API calls. Configure Amazon Bedrock model invocation logging to record detailed model interactions.

Answer: D

Explanation:
Option A is the correct solution because it satisfies authentication, private connectivity, fine-grained authorization, and auditing using AWS-recommended patterns.
SAML federation between Microsoft Entra ID and IAM is a mature, well-supported integration that enables centralized enterprise authentication. Department-specific IAM roles allow precise control over which Bedrock ModelId values each department can invoke, enforcing access by model family.
Using AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services ensures that all inference traffic stays on private AWS network paths, with no public internet exposure. NAT gateways and public endpoints, as used in other options, violate this requirement.
AWS CloudTrail provides authoritative audit logs of all Bedrock API calls, which is required for compliance.
Amazon Bedrock model invocation logging complements CloudTrail by capturing detailed prompt and response metadata for deeper auditing and investigation.
Option B uses public endpoints via NAT. Option C incorrectly claims public endpoints can be private. Option D relies on IdP-side logs, which do not capture Bedrock API activity.
Therefore, Option A is the only solution that fully meets security, compliance, and observability requirements.

NEW QUESTION # 137
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations.
Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real- time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.
Which solution will meet these requirements?

A. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations.
Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.
B. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.
C. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.
D. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.

Answer: D

Explanation:
Option C is the correct solution because it provides near real-time monitoring, hallucination detection, and cost anomaly awareness using built-in Amazon Bedrock and Amazon CloudWatch capabilities, with minimal custom development.
By configuring Amazon Bedrock invocation logging with text output logging, the company captures detailed prompt and response data for auditing and analysis without building custom logging pipelines. This data is stored in Amazon S3, providing durable storage for compliance and retrospective investigation.
Using Amazon Bedrock guardrails with contextual grounding checks allows the application to automatically detect hallucinations by verifying whether generated summaries are grounded in the provided clinical documents. This is the AWS-recommended approach for hallucination detection in RAG and summarization workloads and avoids the need to maintain custom evaluation models or pipelines.
Creating Amazon CloudWatch anomaly detection alarms for InputTokenCount and OutputTokenCount metrics enables automatic detection of abnormal token usage patterns that often correlate with runaway prompts, inefficient summarization, or prompt injection attempts. Anomaly detection adapts dynamically to usage trends, making it more effective than static thresholds for early cost warnings.
Option A introduces batch analytics with Glue and Athena, which is not near real time and increases operational overhead. Option B requires managing evaluation jobs and Lambda-based notification logic.
Option D focuses on infrastructure-level monitoring and offline dashboards rather than near real-time GenAI quality and cost signals.
Therefore, Option C best meets the requirements with the least operational effort and maintenance overhead.

NEW QUESTION # 138
......

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