A company has an Amazon S3 data lake that is governed by AWS Lake Formation. The company wants to create a visualization in Amazon QuickSight by joining the data in the data lake with operational data that is stored in an Amazon Aurora MySQL database. The company wants to enforce column-level authorization so that the company’s marketing team can access only a subset of columns in the database.Which solution will meet these requirements with the LEAST operational overhead?
Answer(s): D
Lake Formation provides centralized, column-level access control across S3 data stored in the data lake and can enforce permissions for QuickSight via Athena as the data source, meeting the requirement with minimal operational overhead.A) EMR would require building/integrating a data pipeline and managing SPICE ingestion and column filtering manually, increasing overhead.B) Glue Studio importing to S3 without built-in column-level authorization tied to Lake Formation lacks governance for per-column access in QuickSight; IAM policies alone cannot enforce column-level data masking at query time.C) Glue Elastic Views creates a materialized view in S3 but does not offer Lake Formation–driven per-column access control for QuickSight; separate policies would be needed.D) Correct: Lake Formation blueprint ingests into S3 and enforces column-level access for QuickSight via Athena, aligning with governance and minimal ops.
A transaction processing company has weekly scripted batch jobs that run on Amazon EC2 instances. The EC2 instances are in an Auto Scaling group. The number of transactions can vary, but the baseline CPU utilization that is noted on each run is at least 60%. The company needs to provision the capacity 30 minutes before the jobs run.Currently, engineers complete this task by manually modifying the Auto Scaling group parameters. The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts. The company needs an automated way to modify the Auto Scaling group’s desired capacity.Which solution will meet these requirements with the LEAST operational overhead?
Answer(s): C
The correct answer is C because predictive scaling uses forecasted demand to pre-provision capacity, allowing 30 minutes lead time before batch jobs and reducing manual intervention; it pre-launches instances to meet anticipated CPU utilization, aligning with least operational overhead. A) Dynamic scaling reacts to current load only and cannot guarantee 30-minute pre-provisioning. B) Scheduled scaling requires fixed times but lacks adaptation to actual demand and accurate forecasting. D) EventBridge/Lambda reacts after the metric hits threshold, causing reactive rather than proactive scaling and adds operational steps. C leverages forecast-based pre-provisioning with minimal ongoing management.
A solutions architect is designing a company’s disaster recovery (DR) architecture. The company has a MySQL database that runs on an Amazon EC2 instance in a private subnet with scheduled backup. The DR design needs to include multiple AWS Regions.Which solution will meet these requirements with the LEAST operational overhead?
The correct answer (C) uses Amazon Aurora Global Database to provide cross-region disaster recovery with low operational overhead. It replicates committed data to a secondary region with low RPO/LRO and automatic failover capabilities, while remaining a managed service with minimal manual intervention.A) Involving multiple EC2 instances requires manual replication setup and failover orchestration; higher operational overhead.B) RDS Multi-AZ with read replication in different AZs does not provide cross-region DR and involves manual promotion in a DR scenario.D) S3 CRR stores backups but requires a restore process; this is a backup-first approach with higher recovery work and latency.
A company has a Java application that uses Amazon Simple Queue Service (Amazon SQS) to parse messages. The application cannot parse messages that are larger than 256 KB in size. The company wants to implement a solution to give the application the ability to parse messages as large as 50 MB.Which solution will meet these requirements with the FEWEST changes to the code?
Answer(s): A
The correct answer is A. The SQS Extended Client Library for Java stores large message payloads in S3 and keeps a pointer in SQS, allowing the application to process messages up to 50 MB with minimal code changes.B is incorrect because EventBridge is not designed to replace SQS for message payloads and would require substantial redesign and integration changes.C is incorrect because SQS has fixed limits; the limit cannot be raised to 50 MB.D is incorrect because SQS does not natively reference EFS; integrating would be more complex and require custom tooling.
A company wants to restrict access to the content of one of its main web applications and to protect the content by using authorization techniques available on AWS. The company wants to implement a serverless architecture and an authentication solution for fewer than 100 users. The solution needs to integrate with the main web application and serve web content globally. The solution must also scale as the company's user base grows while providing the lowest login latency possible.Which solution will meet these requirements MOST cost-effectively?
C) A) is correct.A) Using Amazon Cognito for authentication provides scalable, serverless user pools for up to 100 users with easy federation and app integration. Lambda@Edge enables authorization logic at CloudFront edge locations for low-latency access, and CloudFront serves content globally with caching and edge rules, making it cost-effective and scalable for a global web app.B) Directory Service tied to Active Directory is heavier, not serverless, and ALB is not ideal for global static content delivery at edge. Lambda alone would not provide global edge authorization.C) S3 Transfer Acceleration is for faster uploads/downloads, not suitable for dynamic authorization at edge; lacks identity management.D) Directory Service plus Lambda@Edge adds complexity and Elastic Beanstalk is not the lowest-latency serverless option for global web serving.
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