MLA-C01 · Question #235
A company is using Amazon SageMaker AI to create a classification model to categorize the company's sales performance for each month of the previous 20 years on a scale from 1 to 5. The dataset…
The correct answer is B. Use a stratified split on the month and sales region features. The unusually high sales occur in specific months each year, creating a seasonal pattern that must be represented consistently in both training and validation data. A stratified split by month (and sales region) preserves the distribution of these recurring peak periods across…
Question
A company is using Amazon SageMaker AI to create a classification model to categorize the company’s sales performance for each month of the previous 20 years on a scale from 1 to 5. The dataset includes fields for month, sales region, regional aggregate sales, and the number of stores in each sales region. The company notices that during two months of every year, the aggregate sales values are unexpectedly high. The company performs one-hot encoding on all non-numerical features in the training and validation datasets. The company uses the training dataset to train the classification model. When the company evaluates the model against the validation dataset, the results are less accurate than expected. The company must improve the model’s accuracy on the validation dataset. Which solution will meet this requirement?
Options
- ARemove records that include outliers across all features.
- BUse a stratified split on the month and sales region features.
- CPerform normalization on the aggregate sales feature.
- DPerform normalization on the aggregate sales feature for each sales region.
How the community answered
(34 responses)- A18% (6)
- B71% (24)
- C6% (2)
- D6% (2)
Explanation
The unusually high sales occur in specific months each year, creating a seasonal pattern that must be represented consistently in both training and validation data. A stratified split by month (and sales region) preserves the distribution of these recurring peak periods across both datasets, improving generalization and validation accuracy.
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