AI-102 Practice Question: Implement natural language processing solutions
Which TWO actions should you take to optimize a custom text classification model in Azure Cognitive Service for Language?
⚠ Common exam trap
Test-takers frequently confuse general data preprocessing techniques (like oversampling or stop word removal) with the specific optimization requirements of Azure Cognitive Service for Language's custom text classification, where the service's internal architecture already handles many of these concerns, and the key optimization is ensuring label distinctness and proper data splitting.
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
✓
Ensure that training examples for different labels do not have overlapping content.
Overlapping content between labels (e.g., the same text appearing in both 'positive' and 'negative' training examples) confuses the custom text classification model, leading to poor decision boundaries. Azure Cognitive Service for Language uses a multi-class or multi-label classifier that learns distinct patterns for each label; overlapping content introduces ambiguity, reducing precision and recall. Ensuring distinct, non-overlapping training examples per label helps the model learn clear, separable features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure that training examples for different labels do not have overlapping content.
Why this is correct
Overlapping content confuses the model.
- ✓
Use a stratified split of training and testing data.
Why this is correct
Stratified split maintains class distribution across sets.
- ✗
Oversample the minority classes to balance the dataset.
Why it's wrong here
Oversampling can cause overfitting; better to use techniques like class weighting.
- ✗
Remove all stop words from the training data.
Why it's wrong here
Stop words can be helpful for classification in some contexts.
- ✗
Remove examples with neutral sentiment to focus on positive and negative classes.
Why it's wrong here
Removing data reduces the model's ability to generalize.
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