Quick Answer
The answer is RecordIO-protobuf format with sparse feature vectors. This is the correct choice because Factorization Machines on SageMaker are specifically designed to handle sparse, high-dimensional data, such as user IDs and item IDs in a recommendation system, and the RecordIO-protobuf format allows you to directly encode sparse feature vectors using integer keys and float values. This avoids the memory overhead of dense representations and enables efficient distributed training, which is critical for scaling. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s built-in algorithm optimizations—a common trap is choosing CSV or dense protobuf, which waste memory on zeros. Remember the key: sparse data demands sparse format, and RecordIO-protobuf is the native, optimized vessel for Factorization Machines. A useful memory tip: think “RecordIO for sparse, CSV for dense” to avoid the pitfall.
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
CSV format with one row per rating.
Why wrong: CSV is dense and inefficient for sparse data.
JSON lines format with nested structures.
Why wrong: Supported but not as efficient as protobuf for sparse data.
RecordIO-protobuf format with sparse feature vectors.
Protobuf with sparse encoding is efficient and recommended.
Parquet format with columns for each feature.
Why wrong: Parquet not natively supported by Factorization Machines built-in.
Answer choices
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Parquet format with columns for each feature.
Why it's wrong here
Parquet not natively supported by Factorization Machines built-in.
Common exam traps
The trap here is that candidates 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.
Detailed technical explanation
Under the hood, Factorization Machines model pairwise feature interactions using factorized parameters, which is particularly effective for sparse data like user-item interactions. The RecordIO-protobuf format stores data as a sequence of protobuf messages, each containing a sparse vector of feature indices and values, allowing SageMaker to shard and stream data efficiently across multiple workers. In a real-world scenario, using CSV for a dataset with millions of user and item IDs would create an extremely wide dense matrix, causing memory exhaustion, whereas RecordIO-protobuf with sparse encoding keeps memory usage proportional to the number of non-zero features.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: RecordIO-protobuf format with sparse feature vectors. — Option C is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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