MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is building a recommendation system using Amazon SageMaker. The data is stored in a large S3 bucket with millions of small CSV files. The team wants to train a factorization machines model. Which data ingestion strategy will be MOST efficient?
⚠ Common exam trap
Many exam-takers assume SageMaker can efficiently handle any data format directly from S3, overlooking that factorization machines specifically require RecordIO-wrapped protobuf input for optimal performance with sparse, high-dimensional data.
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
✓
Use a SageMaker Processing job with a Spark container to read the files and write a single RecordIO file.
SageMaker's factorization machines algorithm requires data in RecordIO-wrapped protobuf format for optimal performance, especially with high-dimensional sparse data. Using a SageMaker Processing job with Spark efficiently reads millions of small CSV files from S3, coalesces them into a single or few large RecordIO files, and avoids the overhead of many small S3 GET requests during training, which would otherwise cause severe I/O bottlenecks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a SageMaker Processing job with a Spark container to read the files and write a single RecordIO file.
Why this is correct
Spark can efficiently combine many small files into a single format optimized for training.
- ✗
Use Amazon Athena to query the data and output to a single CSV.
Why it's wrong here
Athena is not designed for converting data to training formats.
- ✗
Point the training job directly to the S3 bucket containing the CSV files.
Why it's wrong here
Training on many small files directly is inefficient due to high I/O overhead.
- ✗
Use SageMaker Data Wrangler to create a data flow and export to a training dataset.
Why it's wrong here
Data Wrangler is for interactive data preparation, not efficient bulk conversion of many files.
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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