DEA-C01 · Question #205
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce. The company…
The correct answer is C. Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales. The correct solution uses Amazon AppFlow to pull data from Salesforce (a managed, no-code connector that eliminates the need for custom API integration code) and AWS Glue to pull and transform the MySQL data, staging both datasets in Amazon S3. AWS Glue then runs the…
Question
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce. The company receives 2 GB of sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night. Which solution will meet these requirements with the LEAST operational overhead?
Options
- AUse Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets.
- BUse Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales
- CUse Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales
- DUse Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data
How the community answered
(59 responses)- A14% (8)
- B3% (2)
- C76% (45)
- D7% (4)
Explanation
The correct solution uses Amazon AppFlow to pull data from Salesforce (a managed, no-code connector that eliminates the need for custom API integration code) and AWS Glue to pull and transform the MySQL data, staging both datasets in Amazon S3. AWS Glue then runs the join/analysis job on a nightly schedule. This combination is fully managed and serverless, minimizing operational overhead. Option A (Amazon MWAA) requires provisioning and managing an Airflow environment, which adds overhead. Option B is likely similar to C but with a less efficient approach (e.g., writing results back to MySQL rather than a data lake). Option D (Kinesis Data Streams) is designed for real-time streaming, which is unnecessary and overly complex for a nightly batch job.
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