AI Associate Data for AI Practice Question
A company uses Einstein Forecasting for revenue prediction. The historical data shows seasonal spikes every quarter. The model consistently underestimates peak periods. What is the best data preparation step to improve 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
✓
Add a 'quarter' index field (1-4) to the dataset.
Einstein Forecasting can detect seasonality if the data contains enough history and a seasonality marker. Adding a 'quarter' feature explicitly helps the model capture recurring patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the forecast horizon to 12 months.
Why it's wrong here
Horizon affects what is predicted, not how well seasonality is captured.
- ✓
Add a 'quarter' index field (1-4) to the dataset.
Why this is correct
Providing explicit seasonality indicators helps the model learn periodic behavior.
- ✗
Remove the spike data points as outliers.
Why it's wrong here
Spikes are patterns, not outliers; removing them would worsen predictions.
- ✗
Use only the last 6 months of data to reduce noise.
Why it's wrong here
Shorter history may lose seasonal patterns.
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