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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implementation and Integration | 26% | - Embedding FMs into applications and workflows - Agent-based AI systems - Prompt engineering and prompt management |
| Topic 2: AI Safety, Security, and Governance | 20% | - Security controls for GenAI applications - Guardrails and responsible AI implementation |
| Topic 3: Testing, Validation, and Troubleshooting | 11% | - Model evaluation and testing - Troubleshooting GenAI systems - Monitoring and observability |
| Topic 4: Operational Efficiency and Optimization for GenAI Applications | 12% | - Performance optimization - Cost optimization and token efficiency |
| Topic 5: Foundation Model Integration, Data Management, and Compliance | 31% | - Compliance and responsible AI practices - Foundation model integration and usage - Data management and RAG architectures |
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問題 #115
A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer's specific issue. Another model extracts key information from the customer interactions. The third model generates responses. The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes. Which solution will meet these requirements?
答案：A
解題說明：
Amazon SageMaker Serverless Inference is specifically designed for applications that experience intermittent or bursty traffic. It automatically scales compute capacity based on the number of requests and scales down to zero when there is no traffic, satisfying the requirement to optimize resource usage during low demand. To meet the 500 ms latency requirement during peak periods and avoid " cold start " delays, provisioned concurrency keeps a specified number of execution environments warm and ready to respond immediately. This provides a balance between the cost-effectiveness of serverless and the performance predictability of provisioned instances. Multi-model endpoints (Option A) can introduce " noisy neighbor " issues and latency spikes, while asynchronous inference (Option D) is intended for long-running workloads and cannot meet sub-500 ms requirements.
問題 #116
A financial services company uses Amazon Bedrock to analyze customer data that is stored in an Amazon S3 bucket. The data includes personally identifiable information (PII). The company must mask PII from foundation model (FM) responses.
Which solution will meet this requirement with the LEAST operational effort?
答案：B
解題說明：
Option A is correct because Amazon Bedrock Guardrails provides a native sensitive-information filter for personally identifiable information. The filter can detect supported PII types and use the MASK action so detected values in model requests or responses are replaced with placeholders such as the PII type. This directly satisfies the requirement to mask PII from foundation model responses with minimal operational effort. It avoids creating and maintaining an additional preprocessing pipeline. Option B can detect or redact PII with Amazon Comprehend, but it requires Lambda logic and data-processing workflows before Bedrock invocation. Options C and D require scanning, copying, and segregating objects across S3 buckets and still do not directly enforce response masking. For response-level protection in Bedrock, the managed Guardrails capability is the simplest and most purpose-built control. AWS Documentation
問題 #117
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?
答案：A
解題說明：
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.
問題 #118
A company runs a generative AI (GenAI)-powered summarization application in an application AWS account that uses Amazon Bedrock. The application architecture includes an Amazon API Gateway REST API that forwards requests to AWS Lambda functions that are attached to private VPC subnets. The application summarizes sensitive customer records that the company stores in a governed data lake in a centralized data storage account. The company has enabled Amazon S3, Amazon Athena, and AWS Glue in the data storage account.
The company must ensure that calls that the application makes to Amazon Bedrock use only private connectivity between the company's application VPC and Amazon Bedrock. The company's data lake must provide fine-grained column-level access across the company's AWS accounts.
Which solution will meet these requirements?
答案：A
解題說明：
The first option labeled B is the correct solution because it fully satisfies both private connectivity and fine- grained cross-account data governance requirements using AWS-native services.
Creating interface VPC endpoints for Amazon Bedrock runtimes ensures that all inference calls remain on the AWS private network and never traverse the public internet. Running AWS Lambda functions in private subnets enforces network isolation, and using IAM conditions that restrict access to specific VPC endpoints and roles prevents unauthorized inference calls.
For the governed data lake, AWS Lake Formation LF-tag-based access control is the recommended AWS mechanism for enforcing cross-account, column-level permissions. LF-tags allow the company to define data access policies once and apply them consistently across accounts, databases, tables, and even individual columns. This is required for sensitive customer records and is not achievable with S3 bucket policies or IAM alone.
The second option labeled B uses a NAT gateway, which violates the private connectivity requirement.
Option C uses public Bedrock endpoints and only database-level grants, which are insufficient. Option D relies on IAM path-based policies, which cannot enforce column-level access and introduces public fallback paths.
Therefore, the first option labeled B is the only solution that meets all networking, security, and data governance requirements.
問題 #119
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?
答案：C
解題說明：
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.
問題 #120
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