- A
Use a single worker with a larger instance type.
Why wrong: A single worker cannot scale horizontally to handle large data volumes.
- B
Increase the number of DPUs and enable job bookmarking.
Why wrong: Job bookmarking helps with incremental processing but doesn't reduce initial full load.
- C
Use JDBC connections with pushdown predicates and increase the number of DPUs.
Pushdown predicates filter data at source, reducing data transfer; more DPUs parallelize the work.
- D
Change the job trigger from time-based to event-based.
Why wrong: Trigger type does not affect job runtime.
Question 767 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses AWS Glue ETL jobs to process data from an Amazon RDS for MySQL database into Amazon S3. The job runs daily and takes 6 hours to complete. The team wants to reduce runtime and cost. The source table has 50 million rows and is updated continuously. Which combination of changes would be MOST effective?
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 JDBC connections with pushdown predicates and increase the number of DPUs.
Option C is correct because using JDBC pushdown predicates filters data at the source database, reducing the volume of data transferred over the network and processed by Glue. Increasing the number of DPUs (data processing units) adds parallelism, which directly reduces runtime. Together, these changes minimize both execution time and cost by optimizing data movement and compute resources.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 a single worker with a larger instance type.
Why it's wrong here
A single worker cannot scale horizontally to handle large data volumes.
- ✗
Increase the number of DPUs and enable job bookmarking.
Why it's wrong here
Job bookmarking helps with incremental processing but doesn't reduce initial full load.
- ✓
Use JDBC connections with pushdown predicates and increase the number of DPUs.
Why this is correct
Pushdown predicates filter data at source, reducing data transfer; more DPUs parallelize the work.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the job trigger from time-based to event-based.
Why it's wrong here
Trigger type does not affect job runtime.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume simply adding more compute (DPUs) or using job bookmarking will solve performance issues, without realizing that the primary bottleneck is data transfer from the source database, which requires predicate pushdown to reduce the data volume.
Detailed technical explanation
How to think about this question
JDBC pushdown predicates in AWS Glue use SQL WHERE clauses to filter rows at the MySQL database engine before data is fetched, leveraging database indexes to reduce I/O and network transfer. Glue's DPU allocation controls the number of Spark executors; increasing DPUs from the default (e.g., 10 to 20) can nearly halve runtime for CPU-bound transformations, but only if data ingestion is not the bottleneck. In practice, for a 50-million-row table, pushdown predicates can reduce the processed row count by orders of magnitude if the filter is selective, making the DPU increase far more effective.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use JDBC connections with pushdown predicates and increase the number of DPUs. — Option C is correct because using JDBC pushdown predicates filters data at the source database, reducing the volume of data transferred over the network and processed by Glue. Increasing the number of DPUs (data processing units) adds parallelism, which directly reduces runtime. Together, these changes minimize both execution time and cost by optimizing data movement and compute resources.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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