Question 529 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A company is training a deep learning model on Amazon SageMaker using a dataset stored in Amazon S3. The training job is taking a long time due to I/O bottlenecks. The data is in JSON lines format. Which data preparation step combined with SageMaker's best practices would most effectively reduce training time?
⚠ Common exam trap
Candidates often assume File mode is always faster because it caches data locally, but they overlook that Pipe mode eliminates the initial download latency entirely, which is the primary cause of I/O bottlenecks in large-scale deep learning training.
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 data to RecordIO-Protobuf format and use SageMaker's Pipe mode for training.
Converting JSON lines data to RecordIO-Protobuf format allows SageMaker's Pipe mode to stream data directly from Amazon S3 to the training algorithm without writing to disk, eliminating I/O bottlenecks. Pipe mode uses a FIFO pipe (named pipe) to feed data sequentially, which significantly reduces training time for deep learning models that iterate over the dataset multiple times.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the JSON lines files to CSV format and use SageMaker's File mode for training.
Why it's wrong here
CSV is not optimized for pipe mode; File mode still loads entire dataset.
- ✗
Compress the JSON lines files using gzip and use File mode with local caching.
Why it's wrong here
Compression reduces storage but File mode still downloads whole files.
- ✓
Convert the data to RecordIO-Protobuf format and use SageMaker's Pipe mode for training.
Why this is correct
RecordIO-Protobuf allows streaming data to the algorithm, minimizing I/O wait.
- ✗
Split the data into multiple smaller files and use multiple training instances to parallelize.
Why it's wrong here
This helps distributed training but does not address I/O per instance.
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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Last reviewed: Jun 24, 2026
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