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

The company wants to use Amazon Kinesis Data Firehose to transform CSV files into JSON format and store the files in Apache Parquet format with the least development effort.

Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3. This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.

Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose’s native format conversion capabilities.

Option C is correct because Amazon OpenSearch Service is designed for search and analytics in near real time with low-latency queries. AWS documentation states that Amazon OpenSearch Service makes it easy to deploy, secure, operate, and scale OpenSearch to search, analyze, and visualize data in real time, and that it provides real-time analytics for use cases such as log analytics, full-text search, application monitoring, and clickstream analytics. That aligns directly with the requirement for handling many requests per second with low query latency.

This option also best satisfies the requirement that individual data teams can own and configure their own environments for cost and performance optimization. By assigning each team a separate OpenSearch cluster, each team can independently tune indexing, storage, shard layout, retention, and scaling policies. Option A is less suitable because Amazon Athena is serverless SQL over data in S3 and is not the best fit for low-latency, high-request-rate interactive search workloads. Option B focuses on streaming data processing, not primary low-latency indexed search. Option D uses a relational database, which is not the native AWS choice for large-scale search and analytics. Therefore, OpenSearch Service is the most appropriate and scalable solution.

 This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. References:

Amazon Redshift streaming ingestion

Creating an external schema for Amazon Kinesis Data Streams

Creating a materialized view for Amazon Kinesis Data Streams