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Question # 1
A company is using Amazon Bedrock to develop an AI-powered application that uses a
foundation model (FM) that supports cross-Region inference and provisioned throughput.
The application must serve users in Europe and North America with consistently low
latency. The application must comply with data residency regulations that require European
user data to remain within Europe-based AWS Regions.
During testing, the application experiences service degradation when Regional traffic
spikes reach service quotas. The company needs a solution that maintains application
resilience and minimizes operational complexity.
Which solution will meet these requirements?
A. Deploy separate Amazon Bedrock instances in North American and European Regions. Use a custom routing layer that directs traffic based on user location. Configure Amazon CloudWatch alarms to monitor Regional service usage. Use Amazon SNS to send email alerts when usage approaches thresholds.
B. Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when calling the InvokeModel API. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints.
C. Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttling. Configure the Lambda functions to call the FM in the nearest secondary Region when quotas are reached.
D. Configure provisioned throughput for Amazon Bedrock in multiple Regions. Implement failover logic in application code to switch Regions when throttling occurs. Use AWS Global Accelerator to route traffic based on user location.
Question # 2
A company is building a multicloud generative AI (GenAI)-powered secret resolution
application that uses Amazon Bedrock and Agent Squad. The application resolves secrets
from multiple sources, including key stores and hardware security modules (HSMs). The
application uses AWS Lambda functions to retrieve secrets from the sources. The
application uses AWS AppConfig to implement dynamic feature gating. The application
supports secret chaining and detects secret drift. The application handles short-lived and
expiring secrets. The application also supports prompt flows for templated instructions. The
application uses AWS Step Functions to orchestrate agents to resolve the secrets and to
manage secret validation and drift detection.
The company finds multiple issues during application testing. The application does not
refresh expired secrets in time for agents to use. The application sends alerts for secret
drift, but agents still use stale data. Prompt flows within the application reuse outdated templates, which cause cascading failures. The company must resolve the performance
issues.
Which solution will meet this requirement?
A. Use Step Functions Map states to run agent workflows in parallel. Pass updated secret metadata through Lambda function outputs. Use AWS AppConfig to version all prompt flows to gate and roll back faulty templates.
B. Use Amazon Bedrock Agents only. Configure Amazon Bedrock guardrails to restrict prompt variation. Use an inline JSON schema for a single agent’s workflow definition to chain tool calls.
C. Use a centralized Amazon EventBridge pipeline to invoke each agent. Store intermediate prompts in Amazon DynamoDB. Resolve agent ordering by using TTL-based backoff and retries.
D. Use Amazon EventBridge Pipes to invoke resolvers based on Amazon CloudWatch log patterns. Store response metadata in DynamoDB with TTL and versioned writes. Use Amazon Q Developer to dynamically generate fallback prompts.
Question # 3
A company is designing a solution that uses foundation models (FMs) to support multiple
AI workloads. Some FMs must be invoked on demand and in real time. Other FMs require
consistent high-throughput access for batch processing.
The solution must support hybrid deployment patterns and run workloads across cloud
infrastructure and on-premises infrastructure to comply with data residency and compliance
requirements.
Which combination of steps will meet these requirements? (Select TWO.)
A. Use AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon SageMaker AI asynchronous endpoints.
B. Configure provisioned throughput in Amazon Bedrock to ensure consistent performance for high-volume workloads.
C. Deploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using Amazon SageMaker Neo. Orchestrate the FMs by using AWS Lambda to support hybrid deployment.
D. Use Amazon Bedrock with auto-scaling to handle unpredictable traffic surges. E. Use Amazon SageMaker JumpStart to host and invoke the FMs.
Question # 4
A company is developing a customer communication platform that uses an AI assistant
powered by an Amazon Bedrock foundation model (FM). The AI assistant summarizes
customer messages and generates initial response drafts.
The company wants to use Amazon Comprehend to implement layered content filtering.
The layered content filtering must prevent sharing of offensive content, protect customer
privacy, and detect potential inappropriate advice solicitation. Inappropriate advice
solicitation includes requests for unethical practices, harmful activities, or manipulative
behaviors.
The solution must maintain acceptable overall response times, so all pre-processing filters
must finish before the content reaches the FM.
Which solution will meet these requirements?
A. Use parallel processing with asynchronous API calls. Use toxicity detection for offensive content. Use prompt safety classification for inappropriate advice solicitation. Use personally identifiable information (PII) detection without redaction.
B. Use custom classification to build an FM that detects offensive content and inappropriate advice solicitation. Apply personally identifiable information (PII) detection as a secondary filter only when messages pass the custom classifier.
C. Deploy a multi-stage process. Configure the process to use prompt safety classification first, then toxicity detection on safe prompts only, and finally personally identifiable information (PII) detection in streaming mode. Route flagged messages through Amazon EventBridge for human review.
D. Use toxicity detection with thresholds configured to 0.5 for all categories. Use parallel processing for both prompt safety classification and personally identifiable information (PII) detection with entity redaction. Apply Amazon CloudWatch alarms to filter metrics.
Question # 5
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. 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. Manually track compliance certifications across Regions.
B. Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region.
C. 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.
D. Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring. Create manual compliance reports for each regulatory jurisdiction.
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