Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

The correct answer is B because AWS Glue streaming ETL jobs are built to continuously consume data from streaming sources such as Amazon Kinesis Data Streams, transform the data, and load the results into Amazon S3 data lakes. The requirement is not just delivery to S3; the telemetry JSON must be flattened before storage, and this needs to happen with low latency. Amazon Data Firehose can deliver streaming data, but the option incorrectly assumes automatic flattening without a transformation layer. SQS plus scheduled batch Glue introduces batch delay and does not meet the lowest-latency requirement. Athena is a query engine, not a real-time transformation and ingestion service for writing flattened streaming output to S3. AWS Glue streaming ETL is the correct serverless streaming transformation layer.

Amazon Redshift data sharing now supports bi-directional and selective write access through datashare namespaces.

This allows centralized management of permissions from headquarters while giving Regional clusters controlled write access to specific datasets — adhering to least privilege and minimizing admin overhead.

“Use Redshift datashares with write permissions and namespaces to enable controlled cross-Region data collaboration while maintaining centralized governance.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This configuration ensures near real-time inventory updates, centralized access management, and compliance with AWS security best practices.

The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to use Amazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.

Amazon Redshift Streaming Ingestion:

Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried using materialized views.

This solution reduces operational complexity because you don ' t need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.