A data engineer needs to combine daily CSV files from an S3 bucket with real-time clickstream events from Amazon Kinesis into a single dataset for analysis. Which AWS service is best suited for this integration task?

Amazon S3 Transfer Acceleration

A retail company receives sales data from multiple sources: a relational database, a NoSQL database, and real-time clickstream events from a web application. They need to combine these into a unified dataset for daily analytics. Which AWS service should they use to perform this integration?

AWS Glue with a scheduled ETL job that reads from all sources and writes to a data lake.

Amazon Kinesis Data Analytics to continuously join streaming and batch data.

Amazon Redshift Spectrum to query data directly from multiple sources.

AWS Lambda functions to read from each source and write to S3.

A data engineering team receives a large CSV file from a third party every day. They need to load this data into Amazon Redshift for analysis. The file contains a mix of data types, including strings, integers, and timestamps. Which approach should they use to load the data efficiently?

Use Amazon EMR to process the CSV and write it to Redshift using JDBC.

Use the Redshift COPY command to load the CSV directly from S3.

Use an AWS Lambda function to insert each row into Redshift.

Use AWS Glue to convert the CSV to Parquet and then load it into Redshift.

AWSCertified Data Engineer - Associate

Domain 1Objective 2

Task 1.2: Transform and Process Data DEA-C01 Practice Questions (Page 1)

Part of the Content Domain 1: Data Ingestion and Transformation domain, which accounts for 34% of the DEA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~17–29 in this domain), expect 4–7 from this objective — we provide 29 practice questions to prepare you well beyond it. (estimate)

29questions here

6free pages

10concepts

34%of the exam

Questions 1–5

1foundation · easy
Which AWS service is used to create, publish, and monitor secure APIs at scale?
Select an answer first
2foundation · easy
A data engineer needs to combine daily CSV files from an S3 bucket with real-time clickstream events from Amazon Kinesis into a single dataset for analysis. Which AWS service is best suited for this integration task?
Select an answer first
3foundation · easy
A data engineer needs to transform a small amount of data (a few MB) in response to an S3 upload event, with minimal latency and no infrastructure to manage. Which AWS service is most appropriate?
Select an answer first
4application · medium
A retail company receives sales data from multiple sources: a relational database, a NoSQL database, and real-time clickstream events from a web application. They need to combine these into a unified dataset for daily analytics. Which AWS service should they use to perform this integration?
Select an answer first
5application · medium
A data engineering team receives a large CSV file from a third party every day. They need to load this data into Amazon Redshift for analysis. The file contains a mix of data types, including strings, integers, and timestamps. Which approach should they use to load the data efficiently?
Select an answer first

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Task 1.2: Transform and Process Data DEA-C01 Practice Questions (Page 1)

Questions 1–5

Which AWS service is used to create, publish, and monitor secure APIs at scale?

A data engineer needs to combine daily CSV files from an S3 bucket with real-time clickstream events from Amazon Kinesis into a single dataset for analysis. Which AWS service is best suited for this integration task?

A data engineer needs to transform a small amount of data (a few MB) in response to an S3 upload event, with minimal latency and no infrastructure to manage. Which AWS service is most appropriate?

A retail company receives sales data from multiple sources: a relational database, a NoSQL database, and real-time clickstream events from a web application. They need to combine these into a unified dataset for daily analytics. Which AWS service should they use to perform this integration?

A data engineering team receives a large CSV file from a third party every day. They need to load this data into Amazon Redshift for analysis. The file contains a mix of data types, including strings, integers, and timestamps. Which approach should they use to load the data efficiently?

Keep studying — Content Domain 1: Data Ingestion and Transformation