MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a large dataset (50 GB) for training a TensorFlow model on SageMaker. The dataset consists of many small CSV files. Training is slow due to I/O bottlenecks. Which data preparation strategy most effectively accelerates training?
⚠ Common exam trap
Candidates often choose larger instances (Option D) as a brute-force fix, failing to recognize that the root cause is the small-file I/O pattern, which requires a format change (TFRecord) rather than more compute resources.
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 dataset to TFRecord format and use tf.data pipeline with prefetching
TFRecord format stores data in a binary, row-oriented format that TensorFlow's tf.data API can read efficiently, especially with prefetching to overlap data loading with model computation. This eliminates the per-file open/parse overhead of many small CSV files, which is the primary cause of I/O bottlenecks in this scenario.
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 dataset to TFRecord format and use tf.data pipeline with prefetching
Why this is correct
TFRecord combines many records into a few large files, and prefetching improves data pipeline efficiency.
- ✗
Convert the dataset to Parquet format and use Apache Arrow for loading
Why it's wrong here
Parquet is good for analytics but not as efficient for TensorFlow training as TFRecord.
- ✗
Compress the CSV files and decompress during data loading
Why it's wrong here
Decompression adds overhead and does not solve file fragmentation.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
This does not address I/O bottlenecks from many small files.
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