MLS-C01 Modeling Practice Question
A data scientist is training a text classification model using Amazon SageMaker's built-in BlazingText algorithm. The dataset contains 1 million documents. Which TWO hyperparameters are most important to tune for improving model accuracy?
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
✓
Learning rate
Learning rate and number of epochs are critical hyperparameters for training neural networks like BlazingText. They control how quickly the model learns and how long it trains.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Learning rate
Why this is correct
Learning rate controls the step size during optimization and is crucial for convergence.
- ✗
Batch size
Why it's wrong here
Batch size can affect training speed and stability, but for BlazingText it's less critical than learning rate and epochs.
- ✗
Loss function
Why it's wrong here
BlazingText uses a fixed loss function (softmax or negative sampling) and does not allow changing it.
- ✗
Type of optimizer
Why it's wrong here
BlazingText uses a fixed optimizer (SGD with negative sampling) and does not allow changing it.
- ✓
Number of epochs
Why this is correct
Number of epochs determines how many times the model sees the training data, affecting under/overfitting.
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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.