You must build an ETL pipeline that ingests files (CSV, JSON, or Parquet) delivered every 15 minutes from 10 source systems into 10 Amazon Redshift tables. All files land in a single S3 bucket; file sizes vary from 10 MB to 20 GB. The pipeline must tolerate schema changes. Which solutions meet these requirements? (Choose two.)

The two correct options leverage AWS Glue workflows, which are ideal for orchestrating multiple dependent jobs and crawlers. The requirement for schema tolerance is met by including an AWS Glue crawler, which automatically infers schema changes and updates the Glue Data Catalog. The first correct option uses an EventBridge rule to trigger the workflow every 15 minutes, aligning with the scheduled ingestion. The second correct option uses a Lambda function triggered by S3 object creation, providing a more event-driven approach for ingestion. Both solutions ensure the crawler runs before the job, allowing for schema evolution. The first incorrect option fails to include a Glue crawler, making it unable to handle schema changes. The third incorrect option overcomplicates the orchestration with multiple Lambda functions and lacks a clear workflow for dependency management. The fourth incorrect option introduces Kinesis Data Firehose, which is unnecessary given that Glue jobs can directly load into Redshift, and it doesn't explicitly include a crawler for schema evolution, though a Glue job could infer schema, it's not as robust for schema changes as a dedicated crawler.