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

The correct answer is D because Amazon Redshift row-level security provides granular access control over sensitive records by applying security policies at the database object level. This directly supports the requirement to let researchers access only records from their own departments without creating multiple copies of the dataset. Multi-condition SQL predicates are stronger than basic department-only filters because they can include department, role, sensitivity, and authorization logic in the same policy design. Creating separate schemas and views increases administrative overhead and can become inconsistent. Datashares are useful for sharing data but the option creates unnecessary departmental copies and does not solve record-level restriction by itself. Granting EXPLAIN RLS to researchers is also not needed for normal secure access.

The correct answer is A because Athena can query new partitions only after the partitions exist in the AWS Glue Data Catalog. Waiting for a nightly crawler creates unnecessary delay. The direct fix is to update the Data Catalog as part of the ETL process by calling the AWS Glue CreatePartition API immediately after each new S3 partition is written. AWS Glue documentation states that CreatePartition creates a new partition and requires the database name, table name, and partition input structure. Copying data to another bucket does not register partitions. DynamoDB Streams is unrelated unless the partition lifecycle is already stored in DynamoDB. StartImportLabelsTaskRun is not the API for partition registration. The correct operational pattern is event/write partition, then immediately register metadata.

This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. References:

Amazon Managed Service for Apache Flink

Apache Flink

Window Aggregations in Flink