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 stores records in a compact binary format, eliminating per-file parsing overhead from thousands of small CSVs. The tf.data pipeline with prefetching overlaps data loading with GPU computation, directly resolving the I/O bottleneck constraining training throughput. This satisfies the scenario's requirement to accelerate training on a 50 GB dataset.
- ✗
Convert the dataset to Parquet format and use Apache Arrow for loading
Why it's wrong here
Parquet with Arrow reduces per-file overhead and enables columnar, zero-copy reads, but the stem's bottleneck is many small CSV files; consolidation into fewer larger objects addresses that directly. Parquet suits column-selective analytical scans, not the per-object overhead dominating this workload.
- ✗
Compress the CSV files and decompress during data loading
Why it's wrong here
Compression shrinks bytes on disk but adds decompression CPU cost and leaves the many-small-files overhead intact, so I/O latency persists. It is tempting because compression reduces transfer volume, which helps bandwidth-bound transfers, yet here per-object read overhead, not volume, is the constraint.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
More vCPUs raise compute throughput but leave the many-small-CSV I/O bottleneck untouched, since each file still incurs per-object overhead. Larger instances are tempting when CPU-bound training is slow, yet here the fix is consolidating files into fewer, columnar objects to cut read operations.
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