MLS-C01 Modeling Practice Question
Which THREE factors should be considered when choosing between SageMaker built-in algorithms and custom algorithms? (Choose THREE.)
⚠ Common exam trap
Watch out — candidates often assume built-in algorithms are limited to CSV/JSON formats and do not support popular frameworks like PyTorch, when in fact SageMaker provides optimized built-in framework containers for PyTorch, TensorFlow, and others, and built-in algorithms support a wide variety of data formats.
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
✓
Custom algorithms allow you to implement any architecture, including proprietary ones
Custom algorithms in SageMaker allow you to implement any architecture, including proprietary or novel models that are not available as built-in algorithms. This flexibility is essential when you need to use a custom neural network, a unique loss function, or a model from a research paper that SageMaker does not natively support.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Custom algorithms allow you to implement any architecture, including proprietary ones
Why this is correct
Custom algorithms offer full flexibility.
- ✓
Built-in algorithms are optimized for distributed training
Why this is correct
Built-in algorithms have built-in distribution strategies.
- ✗
Built-in algorithms can only be used with CSV and JSON formats
Why it's wrong here
They support various formats like RecordIO, protobuf, etc.
- ✗
Custom algorithms require you to bring your own Docker container, but SageMaker built-in algorithms do not support frameworks like PyTorch
Why it's wrong here
Built-in algorithms support TensorFlow, PyTorch, MXNet, etc.
- ✓
Built-in algorithms have predefined hyperparameters that may not fit all use cases
Why this is correct
Custom algorithms allow full hyperparameter control.
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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.