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

The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day’s data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company. References:

Using streaming ingestion with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion

Amazon Athena achieves the fastest query performance when data is stored in columnar formats such as Apache Parquet and when queries can take advantage of partition pruning and predicate pushdown.

Converting CSV files to Parquet significantly reduces the amount of data scanned because Parquet stores data in a column-oriented layout. Since Athena queries only a subset of attributes, it reads only the required columns instead of scanning entire rows, which dramatically improves performance. Predicate pushdown further reduces query time by filtering data at the storage layer.

Partitioning the data by date ensures that Athena scans only the relevant partitions for same-day queries, minimizing unnecessary data reads. Storing one Parquet file per day is efficient and avoids the overhead of managing excessive small files.

ORC is also a columnar format, but Parquet is more commonly optimized and recommended for Athena workloads in AWS exam guidance. JSON and Avro are row-based or semi-row-based formats and result in larger scan sizes and slower query execution.

Therefore, Option D provides the shortest query times and aligns with Athena performance best practices.

Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data.

Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads.

“For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching.”

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