MLS-C01 Data Engineering Practice Question
A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time data. The data source is a Kinesis data stream, and the output is written to an S3 bucket. Recently, the processing latency has increased significantly. The team suspects that the Flink application is encountering backpressure. Which metric should the team monitor to confirm backpressure?
⚠ Common exam trap
It's easy for candidates to confuse `currentLowWatermark` (event time progress) with backpressure detection, or they assume that a high input rate (`numRecordsInPerSecond`) automatically means backpressure, but backpressure is about the operator's inability to keep up, not just the volume of data.
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
✓
busyTimeMsPerSecond
The correct metric to confirm backpressure in a Flink application is `busyTimeMsPerSecond`. This metric measures the percentage of time a task is actively processing data versus waiting for input. A high `busyTimeMsPerSecond` value (close to 1000ms) indicates that the task is fully utilized and cannot keep up with the incoming data rate, which is the direct symptom of backpressure. Other metrics like `currentLowWatermark` relate to event time progress, not backpressure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
currentLowWatermark
Why it's wrong here
Event time progress, not backpressure.
- ✓
busyTimeMsPerSecond
Why this is correct
High busy time indicates operator is overloaded, causing backpressure.
- ✗
numberOfFailedCheckpoints
Why it's wrong here
Checkpoint failures may be a symptom but not a direct measure of backpressure.
- ✗
numRecordsInPerSecond
Why it's wrong here
Throughput metric, not directly indicates backpressure.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.