MLS-C01 Data Engineering Practice Question

A company runs a daily batch ETL job using AWS Glue that reads from Amazon RDS (MySQL), transforms the data, and writes to Amazon Redshift. The job takes 6 hours and processes 500 GB of data. Management wants to reduce the runtime. Which action would be MOST effective?

⚠ Common exam trap

It's easy for candidates to assume the bottleneck is the target database (Redshift) or the source database (RDS), but the question explicitly states the Glue job takes 6 hours, so the compute capacity of Glue itself is the primary constraint.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Increase the number of DPUs allocated to the Glue job

Increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job directly increases the parallelism of the Spark-based ETL job, allowing it to process the 500 GB of data faster. Since the bottleneck is the Glue job's compute capacity, adding more DPUs reduces runtime without changing the source or target infrastructure.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Increase the node size of the Redshift cluster
Why it's wrong here
This only helps with the write speed, but the bottleneck is likely in the Glue transformation.
✗
Use the Redshift COPY command to load data directly from RDS
Why it's wrong here
COPY command is for loading from S3, not directly from RDS.
✗
Use Amazon RDS with Provisioned IOPS SSD storage
Why it's wrong here
The read from RDS is not the bottleneck; the transformation is.
✓
Increase the number of DPUs allocated to the Glue job
Why this is correct
More DPUs allow Glue to process data in parallel, reducing overall runtime.

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MLS-C01 Data Engineering Practice Question

Why each option matters

Option-by-option breakdown

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