MLS-C01 Data Engineering Practice Question
A company runs a daily ETL job that reads data from Amazon RDS, transforms it using AWS Glue, and writes the results to Amazon S3. The job started failing yesterday with the error: 'Rate exceeded'. What is the most likely cause and solution?
⚠ Common exam trap
Many candidates assume 'Rate exceeded' always refers to AWS API throttling (e.g., S3 or Glue API limits) rather than recognizing it as a database connection limit error, especially when the data source is RDS.
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 RDS database is overwhelmed by the number of connections; reduce the Glue job's parallelism or increase RDS instance size
The 'Rate exceeded' error in an AWS Glue job reading from Amazon RDS typically indicates that the database is being overwhelmed by too many concurrent connections or queries. AWS Glue jobs can spawn multiple executors, each opening connections to RDS, and if the database's max_connections or IOPS limit is exceeded, RDS throttles requests. Reducing the Glue job's parallelism (e.g., setting the number of executors or DPUs lower) or scaling up the RDS instance (e.g., increasing instance size or provisioned IOPS) resolves the issue.
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 is using too many DPUs; reduce the number of DPUs
Why it's wrong here
More DPUs increase parallelism, which could hit RDS limits, but reducing DPUs may not be the best solution.
- ✓
The RDS database is overwhelmed by the number of connections; reduce the Glue job's parallelism or increase RDS instance size
Why this is correct
Rate exceeded errors often come from RDS when connection or IO limits are reached.
- ✗
The S3 bucket has reached its request rate limit; request a limit increase
Why it's wrong here
S3 rate limits are very high and unlikely to be exceeded by a single ETL job.
- ✗
Enable job bookmarks in the Glue job to process only new data
Why it's wrong here
Job bookmarks help with incremental processing but do not address rate limits.
Visual reference
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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