The correct choice is to partition the source table by year and use pushdown predicates in the Glue job because this approach directly reduces the volume of data read from PostgreSQL by filtering rows at the database level before transfer. AWS Glue JDBC pushdown predicates performance improves dramatically when predicates like WHERE year = 2024 are pushed down to the source, minimizing network I/O and JDBC driver overhead—the primary bottleneck for a 500-million-row table. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of optimizing Glue ETL jobs by leveraging source-side filtering rather than post-load transformations. A common trap is assuming that increasing Glue worker count alone will solve slow reads, but the real bottleneck is often the JDBC connection and data transfer, not processing power. Remember the memory tip: “Push predicates to the source, not the cluster”—if you can filter at the database, you save time and cost.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 runs a nightly AWS Glue ETL job that reads from a JDBC source (PostgreSQL) and writes to S3 in Parquet format. The job takes over 6 hours, but the SLA requires completion within 4 hours. The source table has 500 million rows and is updated frequently. Which approach will most reliably reduce job duration?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Partition the source table by year and use pushdown predicates in the Glue job.
Option C is correct because partitioning the source table by year and using pushdown predicates allows AWS Glue to read only the relevant partitions from PostgreSQL, drastically reducing the data scanned and transferred. This directly addresses the 500 million row volume and frequent updates by minimizing the JDBC read workload, which is the primary bottleneck in the 6-hour runtime.
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.
✗
Enable job bookmark and schedule the job to run more frequently.
Why it's wrong here
Bookmarks help with incremental processing but not with the initial full load.
✗
Use multiple JDBC connections in parallel by setting 'hashexpression' and 'hashfield'.
Why it's wrong here
Glue supports parallel JDBC reads using hash partitioning; this can improve performance.
✓
Partition the source table by year and use pushdown predicates in the Glue job.
Why this is correct
This reduces the data scanned by filtering on partition columns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of DPUs for the Glue job to 100.
Why it's wrong here
More DPUs can help but are limited by source parallelism and may not reduce time enough.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing DPUs (Option D) or adding parallelism (Option B) will linearly speed up JDBC reads, but they fail to recognize that the bottleneck is the source database's I/O and network throughput, not Glue's compute capacity, and that predicate pushdown is the only option that reduces the data volume at the source.
Detailed technical explanation
How to think about this question
Pushdown predicates in AWS Glue are implemented via the 'pushDownPredicate' option in the JDBC connection, which translates to SQL WHERE clauses sent to PostgreSQL. This leverages database-side filtering, reducing row transfer and leveraging PostgreSQL's index scans if the partition key is indexed. Partitioning the source table by year (e.g., using PostgreSQL's table partitioning or a date column) combined with predicate pushdown ensures that only the relevant year's data is read each run, which is critical for large, frequently updated tables where full scans are prohibitive.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Partition the source table by year and use pushdown predicates in the Glue job. — Option C is correct because partitioning the source table by year and using pushdown predicates allows AWS Glue to read only the relevant partitions from PostgreSQL, drastically reducing the data scanned and transferred. This directly addresses the 500 million row volume and frequent updates by minimizing the JDBC read workload, which is the primary bottleneck in the 6-hour runtime.
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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Question Discussion
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