MLS-C01 Modeling Practice Question
A company is building a recommendation system using Amazon SageMaker's Factorization Machines algorithm. The dataset includes user IDs, item IDs, and ratings. The data is sparse. Which data format should be used for training?
⚠ Common exam trap
Watch out — candidates often assume CSV is always the simplest and most compatible format, overlooking the fact that SageMaker's Factorization Machines specifically require sparse data representation for performance and correctness, making RecordIO-protobuf the only optimal choice among the options.
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
✓
RecordIO-protobuf format with sparse feature vectors.
Factorization Machines (FM) in SageMaker are optimized for sparse, high-dimensional data. The RecordIO-protobuf format allows you to directly specify sparse feature vectors using integer keys and float values, which avoids the memory overhead of dense representations and enables efficient distributed training. This format is the recommended input for SageMaker's built-in FM algorithm.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CSV format with one row per rating.
Why it's wrong here
CSV is dense and inefficient for sparse data.
- ✗
JSON lines format with nested structures.
Why it's wrong here
Supported but not as efficient as protobuf for sparse data.
- ✓
RecordIO-protobuf format with sparse feature vectors.
Why this is correct
Protobuf with sparse encoding is efficient and recommended.
- ✗
Parquet format with columns for each feature.
Why it's wrong here
Parquet not natively supported by Factorization Machines built-in.
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