MLS-C01 Data Engineering Practice Question
A company uses AWS Glue to run ETL jobs that transform data from Amazon RDS for MySQL to Amazon S3. The current job runs daily and takes 3 hours to process 100 GB of data. The company expects data volume to grow 10x in the next year. They need to reduce job runtime and cost. Which approach should they take?
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
✓
Use parallel reads with pushdown predicates in the Glue job's source connection, and write the output in columnar format (Parquet) partitioned by date.
Using parallel reads with pushdown predicates reduces the amount of data transferred from RDS to Glue by filtering at the database level, which lowers extraction time and load on the source. Writing output in columnar format (Parquet) reduces storage size and improves query performance for downstream analytics. Partitioning by date enables efficient pruning. Option A is incorrect because S3 Select is used for server-side filtering of data already in S3, not for tuning extraction from RDS. Option C is incorrect because increasing DPUs alone does not solve the bottleneck from the source database; pushdown predicates are more effective. Option D is incorrect because Redshift Spectrum is used for querying data in S3, not for performing transformations in ETL jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use S3 Select with Glue to filter data before transformation.
Why it's wrong here
S3 Select is used with Athena or applications, not directly within Glue jobs.
- ✓
Use parallel reads with pushdown predicates in the Glue job's source connection, and write the output in columnar format (Parquet) partitioned by date.
Why this is correct
Parallel reads with partition pushdown reduce load on RDS and speed up extraction; Parquet with partitioning reduces storage and query costs.
- ✗
Increase the number of Glue DPUs to 100 and enable job bookmarking.
Why it's wrong here
Increasing DPUs may not improve performance if the source database is the bottleneck; job bookmarking helps with incremental processing but not with current backfill.
- ✗
Use Amazon Redshift Spectrum to perform transformations in place on S3.
Why it's wrong here
Redshift Spectrum is for querying external data, not for transforming it.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.