DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue ETL to transform data from Amazon RDS for PostgreSQL to Amazon S3. The transformation includes joining several tables and aggregating millions of rows. The job runs successfully but takes over 2 hours. The data engineer wants to reduce runtime. Which action is 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
✓
Increase the number of DPUs for the Glue job.
Increasing the number of DPUs (Data Processing Units) in AWS Glue ETL jobs allows more parallel processing of the transformation, which directly reduces runtime for CPU-bound or memory-bound tasks like joining and aggregating millions of rows. Option A: Auto Scaling adjusts DPUs based on workload but does not guarantee maximum performance; it may still be limited by the initial DPU allocation. Option B: DynamicFrames vs DataFrames performance difference is minimal for such operations; this would not significantly reduce runtime. Option D: Converting source data to Parquet applies to data in S3, but the source is Amazon RDS, so this conversion does not help with reading from the database. Therefore, increasing DPUs is the most effective action.
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 Auto Scaling for the Glue job.
Why it's wrong here
Auto Scaling helps but may not be sufficient if the job is not already scaling.
- ✗
Use AWS Glue DynamicFrames instead of DataFrames.
Why it's wrong here
DynamicFrames offer flexibility but not necessarily performance improvement.
- ✓
Increase the number of DPUs for the Glue job.
Why this is correct
More DPUs increase parallelism and reduce execution time.
- ✗
Convert the source data to Parquet format.
Why it's wrong here
The source is RDS, not S3; Parquet is for S3 storage.
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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Same concept, more angles
4 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job reads JSON records and writes Parquet to Amazon S3. Recently, the job started failing with 'Out of Memory' errors. Which change is MOST likely to resolve the issue?
medium- A.Enable compression on the Kinesis stream.
- B.Change the output format from Parquet to ORC.
- ✓ C.Increase the number of DPUs allocated to the Glue job.
- D.Reduce the streaming batch size in the Glue job configuration.
Why C: The 'Out of Memory' error in AWS Glue indicates that the job's allocated resources are insufficient for the data volume or processing complexity. Increasing the number of DPUs (Data Processing Units) directly increases the available memory and compute capacity, which is the most straightforward fix for OOM errors in Glue streaming jobs. Option C is correct because it addresses the root cause—resource exhaustion—by scaling the job horizontally.
Variation 2. A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job fails intermittently with a 'MemoryError'. What is the MOST likely cause?
medium- ✓ A.The Glue job worker type is too small for the data volume
- B.The Glue job uses too many DynamicFrames
- C.The S3 output bucket is in a different region
- D.The Kinesis stream has insufficient shards
Why A: The 'MemoryError' in AWS Glue indicates that the worker type allocated to the job does not have sufficient memory to process the data volume. Glue workers (Standard, G.1X, G.2X) have fixed memory allocations (e.g., 16 GB for Standard), and if the streaming data from Kinesis exceeds this, the job fails. Increasing the worker type or the number of workers resolves this.
Variation 3. A company is using AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job fails intermittently with a 'MemoryError' when the stream has a sudden spike in data volume. Which configuration change would best prevent this error?
medium- ✓ A.Increase the number of DPUs (Data Processing Units) for the Glue job.
- B.Store intermediate results in Amazon RDS.
- C.Use a batch transformation instead of streaming.
- D.Increase the number of shards in the Kinesis data stream.
Why A: Increasing the number of DPUs in the AWS Glue job provides more memory and compute capacity to handle data spikes. Option B is wrong because storing intermediate results in Amazon RDS does not prevent memory errors in Glue; it introduces a database dependency and does not increase Glue's memory. Option C is wrong because switching to batch transformation is not a solution for a streaming job; the job is designed for streaming and batch does not address the memory issue. Option D is wrong because increasing the number of shards in Kinesis increases throughput but does not directly solve memory errors in Glue; it may even increase the data volume per unit time and worsen the problem.
Variation 4. A company uses AWS Glue ETL to process data from Amazon S3 and write results to Amazon Redshift. The job fails with a memory error when processing large files. Which action should the data engineer take to resolve this issue?
medium- A.Reduce the number of partitions in the Glue job.
- ✓ B.Increase the number of DPUs allocated to the Glue job.
- C.Switch to a smaller instance type in the Glue job configuration.
- D.Use S3 Select to filter columns before reading into Glue.
Why B: Increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job provides more memory and compute resources, which directly addresses the out-of-memory error when processing large files. Glue jobs run on Apache Spark, and insufficient DPUs can cause executors to run out of memory during shuffle or aggregation operations on large datasets.
JA
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.