MLS-C01 · Question #242
A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each…
The correct answer is A. Create an AWS Lambda function that can transform the incoming records. Enable data. Amazon Kinesis Data Firehose has a native built-in feature called Data Transformation that invokes an AWS Lambda function synchronously on each batch of records before delivering them to the destination (S3 in this case). This requires zero additional infrastructure - you…
Question
A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model. Which change will create the required transformed records with the LEAST operational overhead?
Options
- ACreate an AWS Lambda function that can transform the incoming records. Enable data
- BDeploy an Amazon EMR cluster that runs Apache Spark and includes the transformation logic.
- CDeploy an Amazon S3 File Gateway in the stores. Update the in-store software to deliver data to
- DLaunch a fleet of Amazon EC2 instances that include the transformation logic. Configure the EC2
How the community answered
(26 responses)- A54% (14)
- B27% (7)
- C8% (2)
- D12% (3)
Explanation
Amazon Kinesis Data Firehose has a native built-in feature called Data Transformation that invokes an AWS Lambda function synchronously on each batch of records before delivering them to the destination (S3 in this case). This requires zero additional infrastructure - you simply write the Lambda transformation logic, enable it in the Firehose configuration, and it runs serverlessly. This is the path of least operational overhead. Option B (EMR with Spark) requires provisioning, managing, and scaling a cluster - heavy overhead for a transformation task. Option C (S3 File Gateway) changes the data delivery mechanism and adds networking complexity. Option D (EC2 fleet) requires provisioning, scaling, patching, and managing instances - significant operational burden compared to a managed Lambda integration.
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