MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a custom TensorFlow model for image classification. The training job runs on a single ml.p3.2xlarge instance. The dataset contains 500,000 images stored in S3. The training time is too long (over 24 hours). The data scientist wants to reduce training time without changing the model architecture. The dataset is already in TFRecord format. The training script uses the default TensorFlow data pipeline. Which change will MOST significantly reduce training time?
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 SageMaker's distributed data parallelism with multiple instances.
Using SageMaker's distributed data parallelism with multiple instances increases the number of GPUs and splits the training data across them, directly reducing the compute time. Option A is incorrect because simply increasing the number of data files does not reduce the computational workload, and Pipe mode primarily helps with streaming data but does not accelerate model training. Option C is incorrect because switching from File to Pipe mode improves data loading but does not address the core compute bottleneck. Option D is incorrect because optimizing the data pipeline with tf.data.Dataset.prefetch and cache can improve I/O efficiency, but the most significant performance gain comes from scaling out the training across multiple GPUs.
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 SageMaker Pipe mode and increase the number of data files.
Why it's wrong here
Increasing files may help parallelism but still limited by single GPU compute.
- ✓
Use SageMaker's distributed data parallelism with multiple instances.
Why this is correct
Distributed training across multiple GPUs significantly reduces wall-clock training time.
- ✗
Switch the input mode from File to Pipe.
Why it's wrong here
Pipe mode may reduce I/O wait but the main bottleneck is computation.
- ✗
Optimize the data pipeline using tf.data.Dataset.prefetch and cache.
Why it's wrong here
Data pipeline optimization can help, but the gain is smaller compared to distributed training.
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 |
Go deeper
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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.