Amazon Web Services AIP-C01 Dumps Questions Answers

Option B is the correct solution because it uses native AWS governance, access control, and auditing capabilities to protect PII while enabling controlled FM access to authorized data subsets. AWS Lake Formation is designed specifically to manage fine-grained permissions for data lakes, including column-level access control, which is critical when handling sensitive financial and PII data.

LF-Tags allow data administrators to define scalable, attribute-based access control policies. By tagging databases, tables, and columns with business unit and Region metadata, the company can enforce policies that ensure the foundation model only accesses approved datasets with PII-redacted columns. This eliminates the risk of sensitive data leaking into production inference workflows.

IAM role-based authentication ensures that the FM accesses data using least-privilege credentials. This integrates cleanly with Amazon Bedrock, which supports IAM-based authorization for service-to-service access. AWS CloudTrail provides immutable audit logs for all access attempts, satisfying compliance and regulatory requirements.

Option A introduces unnecessary data duplication and weak governance controls. Option C relies on custom application logic, increasing operational risk and complexity. Option D bypasses Lake Formation’s fine-grained controls and relies on presigned URLs, which reduces governance visibility and control.

Therefore, Option B best meets the requirements for security, compliance, scalability, and auditability when integrating Amazon Bedrock with a Lake Formation–governed data lake.

Option B best fulfills all functional, scalability, and collaboration requirements by combining purpose-built AWS services with Amazon Bedrock capabilities. Amazon Bedrock Data Automation is designed to orchestrate large-scale, multimodal data processing pipelines and integrates naturally with foundation models for summarization and concept extraction. Using BDA to process document files ensures consistent preprocessing and model invocation at scale, which is essential for handling more than 10,000 sources per day with high concurrency.

Integrating Amazon Textract for PDFs enables accurate extraction of structured and unstructured text from scanned and digital documents, while Amazon Transcribe is the appropriate service for converting recorded videos into text for downstream semantic analysis. These services are optimized for their respective media types and feed clean, normalized inputs into Bedrock foundation models, improving the quality of contextual summaries.

Storing processed content in Amazon S3 with versioning enabled directly addresses the requirement for version control. S3 versioning provides immutable object history and rollback capabilities without additional complexity. Metadata storage in Amazon DynamoDB supports high-throughput, low-latency access patterns and scales automatically to handle peak upload concurrency.

Real-time collaboration is achieved through AWS AppSync GraphQL subscriptions combined with DynamoDB. AppSync enables real-time updates to connected clients whenever study materials are created or modified, making it well suited for collaborative editing and live synchronization. DynamoDB streams integrate seamlessly with AppSync to propagate changes efficiently.

The other options misuse services or fail to meet key requirements. Amazon SNS does not support collaborative state synchronization, Amazon DocumentDB is not optimized for versioned document storage, Amazon Neptune is unsuitable for document-centric workloads, and Amazon ElastiCache is not designed for durable storage or version control. Option B aligns with AWS best practices for scalable, multimodal generative AI systems built on Amazon Bedrock.

Option B is correct because the application is currently invoking the base foundation model identifier, which routes traffic to the on-demand capacity pool rather than the company’s purchased provisioned throughput. In Amazon Bedrock, provisioned throughput is attached to a specific provisioned resource created through the provisioned throughput APIs. To consume that reserved capacity, inference requests must target the provisioned resource identifier that represents the purchased throughput, not the generic model identifier used for on-demand inference.

The code snippet uses modelId= " anthropic.claude-v2 " . This value selects the on-demand endpoint for that model. As a result, requests are subject to on-demand quotas and throttling behavior, while the provisioned throughput remains idle. This directly explains the CloudWatch observation: provisioned capacity metrics show unused capacity because no traffic is being directed to the provisioned resource, and the on-demand path is throttling because it is exceeding the applicable on-demand limits during peak volume.

Replacing the modelId value with the provisioned throughput ARN returned by the CreateProvisionedModelThroughput workflow ensures the runtime invocation is routed to the reserved capacity. Once traffic is directed correctly, the purchased model units provide the consistent throughput required for predictable performance during business hours, which is exactly why provisioned throughput is used.

Option A could increase capacity, but it does not fix the core issue that the application is not using the provisioned resource at all. Option C can reduce the impact of throttling temporarily, but it adds latency and does not guarantee consistent throughput; it also still wastes the provisioned capacity. Option D changes the response delivery mechanism, but throttling is a capacity routing and quota issue, not a streaming API issue.