MLS-C01 Data Engineering Practice Question
Network Topology
Refer to the exhibit. An ML engineer runs the above CLI command to inspect files in an S3 bucket. The training data consists of 200 CSV files, each 1 GB. The engineer plans to use Amazon SageMaker to train a model using this data. What should the engineer do to optimize training performance?
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
✓
Convert the CSV files to Parquet format and use Pipe input mode.
Converting CSV files to Parquet format and using Pipe input mode significantly improves training performance. Parquet is a columnar storage format that reduces I/O by reading only relevant columns, and it is compressed. Pipe input mode streams data directly from S3 to the training algorithm without downloading to EBS, reducing startup time and disk usage. Option A is incorrect because simply increasing the number of instances does not address the inefficiency of reading CSV files; it may help parallelization but not per-instance throughput. Option B is incorrect because Amazon Athena is a query service, not a data transformation tool for SageMaker; converting to CSV with headers does not improve performance. Option C is incorrect because using File input mode copies all files to the training instance's EBS volume, which is slow for 200 GB of data and does not leverage streaming benefits.
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 number of training instances to process files in parallel.
Why it's wrong here
More instances help but do not optimize data reading.
- ✗
Use Amazon Athena to transform the data into CSV format with headers.
Why it's wrong here
Athena is not used for training data preparation.
- ✗
Use the File input mode and copy all files to the training instance's EBS volume.
Why it's wrong here
File mode downloads all data first, causing delays for large datasets.
- ✓
Convert the CSV files to Parquet format and use Pipe input mode.
Why this is correct
Parquet is columnar and compressed; Pipe mode streams data directly from S3.
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.