Question 487 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a model on Amazon SageMaker and notices that the training job is taking much longer than expected. The instance type is ml.m5.xlarge and the dataset is 10 GB in CSV format. Which action is MOST likely to reduce training time without changing the instance type?
⚠ Common exam trap
A common mix-up: candidates assume that changing the instance type (Option A) to a GPU instance is the only way to speed up training on SageMaker, or that hyperparameter tuning (like reducing epochs or increasing batch size) is always effective, but the question specifically tests understanding of data format optimization (e.g., using Parquet or RecordIO) as a cost-effective and instance-agnostic method to reduce I/O bottlenecks in SageMaker.
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 RecordIO or Parquet format before training.
Converting the dataset from CSV to a columnar format like Parquet or RecordIO reduces I/O overhead and improves data throughput during training. SageMaker's built-in algorithms and many deep learning frameworks can read these formats more efficiently, especially for large datasets, because they enable better compression and predicate pushdown, reducing the time spent on data loading.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the instance type to ml.p3.2xlarge (GPU) for faster computation.
Why it's wrong here
The stem specifies not changing instance type, and GPU may not be beneficial for all algorithms.
- ✗
Reduce the number of training epochs to speed up convergence.
Why it's wrong here
Reducing epochs may degrade model performance and does not address the root cause of slow training.
- ✓
Convert the dataset to RecordIO or Parquet format before training.
Why this is correct
RecordIO and Parquet are columnar formats that reduce I/O and allow faster data loading in SageMaker.
- ✗
Increase the batch size to the maximum supported by the instance memory.
Why it's wrong here
Increasing batch size may improve GPU utilization but does not directly reduce I/O bottlenecks from CSV parsing.
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Last reviewed: Jul 4, 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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