DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue to run an ETL job that reads data from Amazon DynamoDB and writes to Amazon Redshift. The job fails with a 'ThroughputExceededException' error. What is the most likely cause?
⚠ Common exam trap
DEA-C01 often tests whether candidates correctly attribute throttling errors to the source database (DynamoDB) rather than the target (Redshift) or the Glue job configuration; the exception name itself points to DynamoDB throughput.
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
✓
The DynamoDB table's read capacity is insufficient for the Glue job's read rate
A ThroughputExceededException from DynamoDB indicates that the read or write request rate exceeded the provisioned throughput (RCUs/WCUs) or the burst capacity of the table or index. When AWS Glue reads from DynamoDB, it consumes read capacity units; if the table's read capacity is too low for the parallel read rate of the Glue job, DynamoDB throttles the requests and the job fails.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Glue job has a timeout setting that is too low
Why it's wrong here
A timeout terminates a job with a timeout error, not ThroughputExceededException, which DynamoDB returns when read or write capacity is exceeded. Raising the timeout leaves the throttling untouched. Timeout tuning is the right fix when a job is cancelled mid-run despite adequate provisioned capacity.
- ✗
The Redshift cluster's concurrency scaling is insufficient
Why it's wrong here
Concurrency scaling adds Redshift compute for queued queries; it cannot produce a DynamoDB ThroughputExceededException, which originates from the source table's partition-level capacity. Concurrency scaling is the correct remedy when Redshift write queries queue behind other Redshift workloads.
- ✓
The DynamoDB table's read capacity is insufficient for the Glue job's read rate
Why this is correct
DynamoDB returns ThroughputExceededException when read requests exceed the table's provisioned or on-demand read capacity. Glue's parallel executors consume read capacity units rapidly, exhausting the table's limits. This directly satisfies the stem's constraint: the read rate from DynamoDB surpasses available capacity, causing throttling rather than a Redshift or Glue configuration fault.
- ✗
The S3 bucket where Glue writes temporary data does not have proper permissions
Why it's wrong here
Missing S3 permissions cause AccessDenied errors during Glue's spill or shuffle operations, not ThroughputExceededException, which DynamoDB raises when partition capacity is exceeded. S3 permission fixes apply when Glue jobs fail writing temporary or output objects to their bucket.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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