AI Associate AI Fundamentals Practice Question
A retail company wants to use AI to predict next month's sales for each product category. They have five years of monthly sales data. Which THREE factors are most critical for the accuracy of the predictive model? (Select THREE.)
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
✓
Including seasonality and trend components from historical data
Historical patterns (seasonality), external factors (holidays), and data quality are key for time series forecasting. Model complexity and algorithm brand are less critical.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Choosing the most popular AI algorithm on the market
Why it's wrong here
Popularity does not guarantee suitability; model selection should be based on problem and data.
- ✓
Including seasonality and trend components from historical data
Why this is correct
Critical for capturing recurring patterns.
- ✗
Using a deep learning model instead of a simpler model
Why it's wrong here
With only five years of monthly sales data—60 data points—a deep learning model would suffer from high variance and overfitting due to insufficient training samples for its many parameters. It is tempting because deep learning excels at capturing complex, non-linear patterns in massive datasets, such as image or text sequences, where millions of examples are available.
- ✓
Ensuring the training data is clean and free of missing values
Why this is correct
Garbage in, garbage out — data quality is essential.
- ✓
Incorporating external factors like holiday calendars and promotions
Why this is correct
External events significantly impact sales.
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