MLS-C01 Data Engineering Practice Question
A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time sensor data. The application reads from a Kinesis data stream, performs windowed aggregations, and writes results to an S3 bucket. Recently, the application has been experiencing high latency and checkpoint failures. What is the MOST likely cause?
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 S3 destination bucket is located in a different AWS Region than the Kinesis application
The S3 destination bucket is located in a different AWS Region than the Kinesis application. Cross-region data transfer introduces network latency and increases the likelihood of checkpoint failures because Apache Flink checkpoints require writes to complete within a timeout. Option A (insufficient shards) would cause throttling (ProvisionedThroughputExceededException) but not directly checkpoint failures. Option C (record size > 1 MB) is impossible because Kinesis Data Streams enforces a 1 MB maximum record size. Option D (low parallelism) could cause backpressure but not typically checkpoint failures unless resources are severely constrained. Therefore, the most likely cause is the cross-region S3 bucket.
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 number of shards in the Kinesis stream is insufficient for the data volume
Why it's wrong here
Insufficient shards cause throttling, not checkpoint failures.
- ✓
The S3 destination bucket is located in a different AWS Region than the Kinesis application
Why this is correct
Cross-region writes increase latency and can cause checkpoint timeouts.
- ✗
The record size in the Kinesis stream exceeds the 1 MB limit
Why it's wrong here
Kinesis enforces a 1 MB limit; exceeding it would cause write errors.
- ✗
The parallelism of the Flink application is set too low for the number of shards
Why it's wrong here
Low parallelism may cause backpressure but not checkpoint failures directly.
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
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