AI0-001 AI Implementation and Operations Practice Question
A retail company uses a time-series model to forecast daily sales for inventory management. The model is a seasonal ARIMA trained on three years of daily data. It performed well during initial validation but after deployment, forecasts became inaccurate during holiday seasons, often underestimating demand by up to 40%. The data science team examined the features and found that the training data did not include any holiday indicators. They also discovered that the model's residuals show strong autocorrelation during holiday weeks. The company needs to improve the forecast for the upcoming holiday season. They have access to historical sales data with holiday dates and are considering several approaches. Which approach will BEST address the issue?
⚠ Common exam trap
CompTIA often tests the misconception that adding more data or switching to a more complex model (like LSTM) automatically fixes forecasting issues, when the real problem is missing relevant features that can be addressed with a simpler, interpretable approach like SARIMAX with exogenous variables.
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
✓
Engineer holiday-related features (e.g., holiday flag, days before/after) and retrain the ARIMA model with these features as exogenous variables.
The core issue is that the ARIMA model lacks holiday-related information, causing systematic underestimation during holiday periods. By engineering holiday features (e.g., binary flags, days-before/after indicators) and including them as exogenous variables in a SARIMAX model, the model can directly learn the demand spikes associated with holidays. This directly addresses the residual autocorrelation during holiday weeks and improves forecast accuracy without changing the underlying time-series framework.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Engineer holiday-related features (e.g., holiday flag, days before/after) and retrain the ARIMA model with these features as exogenous variables.
Why this is correct
Incorporating holiday information directly addresses the root cause of the forecast error.
- ✗
Decrease the learning rate and increase the number of training epochs.
Why it's wrong here
ARIMA is not trained via gradient descent; learning rate and epochs are not applicable.
- ✗
Create an ensemble of the ARIMA model and a seasonal naive model to average forecasts.
Why it's wrong here
An ensemble may smooth errors but does not correct the systematic underestimation during holidays.
- ✗
Replace the ARIMA model with a deep learning LSTM model trained on the same data.
Why it's wrong here
Simply switching to a complex model does not guarantee improvement if the missing features are not added.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.