DEA-C01 Data Ingestion and Transformation Practice Question
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?
⚠ Common exam trap
The trap is confusing stream-side scaling (adding Kinesis shards) with job-side scaling (adding Glue DPUs), leading candidates to pick D when the error is in the Glue job's memory.
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 (Data Processing Units) for the Glue job.
Increasing the number of DPUs for the AWS Glue job provides more compute and memory resources to the job, which directly addresses the MemoryError caused by sudden data volume spikes. Glue DPUs are the unit of processing capacity, and scaling them up allows the job to handle larger volumes without running out of memory. This is the most direct configuration change to prevent memory-related failures.
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 number of DPUs (Data Processing Units) for the Glue job.
Why this is correct
MemoryError arises when executors exhaust heap during volume spikes. Adding DPUs increases the number of workers and total memory available, spreading the load across more executors. This directly addresses the memory constraint rather than tuning batch size or windowing.
- ✗
Store intermediate results in Amazon RDS.
Why it's wrong here
RDS stores relational rows, not Glue shuffle or spill data, so it cannot relieve executor memory pressure during a spike. It suits persistent application state or reporting; Glue handles intermediate state through S3 spill and job bookmarks instead.
- ✗
Use a batch transformation instead of streaming.
Why it's wrong here
Switching to batch discards the continuous Kinesis consumption model the pipeline requires, so spikes are never absorbed in real time. Batch suits scheduled, bounded datasets; the fix here is raising worker memory and enabling job bookmarks with bounded windowing to smooth spikes.
- ✗
Increase the number of shards in the Kinesis data stream.
Why it's wrong here
More shards raise Kinesis ingest throughput, feeding the Glue job faster and worsening the MemoryError rather than relieving it. Shard scaling suits insufficient stream capacity; the constraint here is worker-side memory, addressed by increasing worker size or count.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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