MLS-C01 Modeling Practice Question
A data scientist is building a recommendation system using collaborative filtering. The dataset contains user-item interactions in a sparse matrix. The model will be trained on Amazon SageMaker using the built-in Factorization Machines algorithm. Which data format should the scientist use for the training 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
✓
RecordIO-protobuf format with sparse features
Amazon SageMaker's Factorization Machines algorithm expects input in the 'application/x-recordio-protobuf' format for sparse data, or in CSV format for dense data. For sparse data, Protobuf is recommended for performance.
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 all features as columns
Why it's wrong here
Factorization Machines require the input data to be in the format of sparse feature vectors, typically using the protobuf recordIO format with float32 tensors or the libsvm format, because the algorithm processes only non-zero entries to handle high-dimensional sparse matrices efficiently. CSV with all features as columns would force dense representation, expanding memory and compute costs unnecessarily. This option tempts because CSV is a universal, human-readable format often used for tabular data in standard regression or classification tasks, where dense feature matrices are acceptable.
- ✗
JSON format with nested arrays
Why it's wrong here
JSON is not a native format for SageMaker built-in algorithms.
- ✓
RecordIO-protobuf format with sparse features
Why this is correct
RecordIO-protobuf is the recommended format for sparse data for Factorization Machines.
- ✗
Parquet format
Why it's wrong here
Parquet is not directly supported by Factorization Machines.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases) and want to use matrix factorization. They plan to use Amazon SageMaker to train the model. Which dataset format is MOST appropriate for the built-in Factorization Machines algorithm?
medium- A.Libsvm format with user_id and item_id as features
- B.CSV file with user_id, item_id, and label columns
- ✓ C.RecordIO-protobuf with user_id, item_id, and label fields
- D.JSON lines file with user_id, item_id, and label fields
Why C: The built-in Factorization Machines algorithm in Amazon SageMaker requires the RecordIO-protobuf format for optimal performance, as it allows efficient binary serialization and direct integration with SageMaker's distributed training infrastructure. This format supports sparse data representation, which is critical for high-dimensional user-item interaction data, and enables faster I/O and reduced memory overhead compared to text-based formats.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.