DEA-C01 Data Operations and Support Practice Question
A company uses AWS Glue to run ETL jobs that process data from an Amazon RDS for MySQL database and load it into an Amazon S3 data lake. The Glue job runs daily and processes incremental data. Recently, the job has been taking longer than expected. The engineer checks the CloudWatch logs and sees that the job is spending most of its time on the 'Reading from JDBC' phase. The MySQL table has 10 million rows and is indexed on the primary key. The Glue job uses a 'job bookmark' to track processed data. The engineer wants to improve the performance of the read phase. Which action is most likely to help?
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
✓
Modify the job to use a 'query' parameter that selects only the new or modified rows based on a timestamp column.
Modifying the job to use a 'query' parameter with a WHERE clause that filters on a timestamp column (the bookmark key) allows AWS Glue to read only the new or modified rows, reducing the amount of data transferred from the database and significantly improving read performance. Option A is wrong because increasing the JDBC 'fetchSize' parameter can improve throughput per connection but does not address the root cause of reading all 10 million rows; it may also cause memory issues if set too high. Option B is wrong because disabling job bookmarks and performing a full refresh would reprocess all data, making the job even slower and defeating the purpose of incremental processing. Option C is wrong because increasing the number of DPUs adds parallelism but does not reduce the volume of data read; the database can still become a bottleneck when reading the entire table.
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 JDBC 'fetchSize' parameter to 10000.
Why it's wrong here
A larger fetch size reduces round trips but each fetch retrieves more data, which may not help if the issue is data volume.
- ✗
Disable job bookmark and perform a full refresh each time.
Why it's wrong here
Full refresh would read all 10 million rows every run, making the job slower.
- ✗
Increase the number of DPUs for the Glue job.
Why it's wrong here
More DPUs increase parallelism, but the database may not be able to handle multiple concurrent connections efficiently.
- ✓
Modify the job to use a 'query' parameter that selects only the new or modified rows based on a timestamp column.
Why this is correct
By filtering at the source, less data is read and transferred, speeding up the read phase.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-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 DEA-C01 exam.