MLS-C01 Modeling Practice Question
A data scientist is training a text classification model using Amazon SageMaker's BlazingText algorithm. The dataset consists of 1 million documents, each labeled with one of 10 categories. The model achieves 92% accuracy on a held-out test set. However, when deployed, the model performs poorly on documents containing slang and typos. What should the data scientist do to improve model robustness?
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
✓
Augment the training data by introducing common slang replacements and typos.
Data augmentation by introducing common slang replacements and typos into the training data increases the model's robustness to such variations, helping it generalize better to real-world text that contains slang and typos. Removing such documents (Option A) reduces the training data and does not teach the model to handle these variations. Increasing the embedding dimension (Option C) or number of epochs (Option D) does not directly address the issue of slang and typos.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all documents with slang or typos from the training set.
Why it's wrong here
Removing them reduces data and does not teach the model to handle such inputs.
- ✓
Augment the training data by introducing common slang replacements and typos.
Why this is correct
Data augmentation exposes the model to realistic noise, improving robustness.
- ✗
Increase the embedding dimension from 100 to 300.
Why it's wrong here
Larger embeddings may capture more semantics but do not specifically address slang/typos.
- ✗
Increase the number of training epochs.
Why it's wrong here
More epochs may overfit to the clean training data, worsening performance on noisy inputs.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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