DEA-C01 Data Operations and Support Practice Question
A company runs a data pipeline that ingests clickstream data from a web application into Amazon Kinesis Data Streams. A Lambda function processes records from the stream and writes them to an Amazon S3 bucket in JSON format. The pipeline has been running smoothly, but for the past hour, the Lambda function has been failing with 'Rate exceeded' errors, and the Kinesis stream shows elevated 'IteratorAgeMilliseconds' metrics. The Lambda function has a reserved concurrency of 100, and the Kinesis stream has 10 shards. The average record size is 5 KB, and the data rate is approximately 15 MB per second. Which combination of actions should a data engineer take to resolve the issue and prevent recurrence? (Choose TWO.)
⚠ Common exam trap
The trap here is that candidates often focus only on Lambda concurrency (Option A) and overlook the Kinesis shard count (Option B), not realizing that both the consumer (Lambda) and the stream capacity must be scaled together to resolve throughput bottlenecks.
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 Lambda function's reserved concurrency to 200.
The 'Rate exceeded' errors indicate that the Lambda function's concurrency is insufficient to keep up with the incoming data rate from Kinesis. With 10 shards and a 15 MB/s data rate, each shard processes ~1.5 MB/s, and with 5 KB records, that's ~300 records per second per shard. Increasing reserved concurrency to 200 allows more parallel invocations to handle the load, reducing the iterator age.
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 Lambda function's reserved concurrency to 200.
Why this is correct
More concurrency allows more parallel invocations to process records faster.
- ✓
Increase the number of Kinesis shards to 20.
Why this is correct
More shards increase the stream's ingestion capacity and reduce IteratorAgeMilliseconds.
- ✗
Decrease the Lambda function's batch size from 100 to 50.
Why it's wrong here
Smaller batch size means more invocations, increasing rate of API calls and potentially worsening rate limits.
- ✗
Enable S3 multipart upload for the Lambda function.
Why it's wrong here
Multipart upload is for large objects, not for rate limiting issues; the problem is upstream of S3.
- ✗
Replace the Lambda function with Amazon Kinesis Data Firehose to write directly to S3.
Why it's wrong here
This changes the architecture but does not address the immediate rate limiting; also, KDF has its own 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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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.