- A
Use a JDBC connection with a higher batch size for writing to Redshift.
Larger batch sizes reduce round trips and improve write throughput.
- B
Partition the input data in S3 by date or category.
Partitioning allows Glue to read only relevant data.
- C
Switch to a single-node Redshift cluster to reduce latency.
Why wrong: Single-node reduces parallelism and may worsen performance.
- D
Increase the number of DPUs allocated to the Glue job.
More DPUs provide more parallelism for processing.
- E
Reduce the number of input files by combining them into larger files.
Why wrong: Fewer files can reduce parallelism.
Question 1,189 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data engineer is troubleshooting a slow-running AWS Glue ETL job that reads from Amazon S3 and writes to Amazon Redshift. The job processes 500 GB of CSV data daily. The engineer wants to improve performance. Which THREE actions should the engineer take? (Choose three.)
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 a JDBC connection with a higher batch size for writing to Redshift.
Option A is correct because increasing the JDBC batch size for the Redshift connection reduces the number of network round trips and improves write throughput. The Glue JDBC connector batches rows into a single INSERT statement; a larger batch size (e.g., 1000 instead of the default 100) allows more rows per commit, reducing overhead and speeding up the write phase.
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 JDBC connection with a higher batch size for writing to Redshift.
Why this is correct
Larger batch sizes reduce round trips and improve write throughput.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Partition the input data in S3 by date or category.
Why this is correct
Partitioning allows Glue to read only relevant data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a single-node Redshift cluster to reduce latency.
Why it's wrong here
Single-node reduces parallelism and may worsen performance.
- ✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
More DPUs provide more parallelism for processing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the number of input files by combining them into larger files.
Why it's wrong here
Fewer files can reduce parallelism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume combining files always improves performance (due to Hadoop's small file problem), but in Glue ETL with Spark, moderate parallelism from many files is beneficial, and the real bottleneck is often the JDBC write path, not the S3 read path.
Detailed technical explanation
How to think about this question
Under the hood, Glue ETL uses Spark's JDBC connector, which partitions writes based on the number of Spark partitions and the JDBC batch size. A higher batch size reduces the number of INSERT statements but must be balanced against Redshift's WLM queue limits and the risk of memory pressure. In real-world scenarios, tuning the batch size to 1000–5000 often yields 2–3x speed improvements for large loads, but exceeding Redshift's max rows per INSERT (32,767) will cause errors.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a JDBC connection with a higher batch size for writing to Redshift. — Option A is correct because increasing the JDBC batch size for the Redshift connection reduces the number of network round trips and improves write throughput. The Glue JDBC connector batches rows into a single INSERT statement; a larger batch size (e.g., 1000 instead of the default 100) allows more rows per commit, reducing overhead and speeding up the write phase.
What should I do if I get this DEA-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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