AWS Certified Data Engineer - Associate DEA-C01Demo

By Amazon · Question Mode

QuestionQ220

Data Security and Governance

A security company stores IoT data in JSON format in an Amazon S3 bucket. The data structure may change when the company upgrades its IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.

Which solution meets these requirements in the MOST cost-effective way?

  • A Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
  • B Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.
  • C Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.
  • D Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
Explanation

An AWS Glue Data Catalog paired with a Glue Schema Registry provides a managed metadata store that can track and version evolving JSON schemas from IoT devices, while a Glue workload ingests that data into Redshift Serverless for on-demand analytics without the cost of a provisioned cluster. Athena workgroups alone do not create or maintain a data catalog, and the Lambda/Step Functions approach in the other option adds unnecessary orchestration complexity and cost.

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