MLA-C01 Data Preparation for Machine Learning Practice Question
A company operates an IoT platform that ingests sensor data from thousands of devices. Data is streamed via Amazon Kinesis Data Streams and stored in an S3 bucket using a Kinesis Firehose delivery stream, which writes data in 5-minute windows. The data is then used to train a machine learning model for anomaly detection. Recently, the data science team noticed that the training dataset is always missing the last 5 minutes of events from the end of each day. The S3 objects show that the last delivery stream buffer window is incomplete. The data engineer checked the Kinesis Firehose metrics and found no delivery errors or data loss, but the 'IncomingBytes' and 'IncomingRecords' metrics show consistent data for all periods. The S3 bucket has Lifecycle policies that do not delete objects. The team suspects the issue is related to the data preparation pipeline. Which course of action would correctly resolve the missing data problem?
⚠ Common exam trap
It's easy for candidates to assume the missing data is due to data loss or pipeline errors, but the real issue is that Firehose's buffer window never completes when data stops arriving, so no S3 object is created for that final period.
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 buffer size to 10 MB and reduce the buffer interval to 60 seconds in the Firehose delivery stream configuration
The issue is that the last 5-minute buffer window at the end of each day never completes, so Firehose never delivers that final object to S3. By reducing the buffer interval to 60 seconds and increasing the buffer size to 10 MB, Firehose will flush data more frequently, ensuring that even small residual data at the end of the day is delivered before the stream stops. This directly addresses the incomplete last window without requiring reprocessing or changing the pipeline architecture.
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 buffer size to 10 MB and reduce the buffer interval to 60 seconds in the Firehose delivery stream configuration
Why this is correct
Reducing the buffer interval to 60 seconds ensures that data is flushed every minute, preventing incomplete windows from being missed at the end of the day.
- ✗
Reprocess the Kinesis stream data from the beginning using a custom application
Why it's wrong here
Reprocessing does not address the root cause of incomplete buffer windows.
- ✗
Modify the data preparation pipeline to use AWS Lambda to write data to S3 directly from Kinesis
Why it's wrong here
This is a significant architectural change and not necessary; the issue is easily fixed by adjusting Firehose settings.
- ✗
Increase the buffer interval to 600 seconds to allow more time for data to accumulate
Why it's wrong here
Increasing the buffer interval would make the problem worse by delaying flushes further.
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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