AI0-001 AI Concepts and Techniques Practice Question
A small logistics company wants to forecast next month's shipment volume using three years of historical monthly totals. The operations manager notes that volume has grown steadily and that December is always the busiest month. The data science consultant recommends a classical time series method that explicitly separates the long-term upward movement from the repeating yearly pattern. Which technique BEST fits this requirement?
⚠ Common exam trap
The trap here is reaching for a flexible machine learning model by default, when classical time series methods like SARIMA are the correct tool for short series with clear trend and seasonality.
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
✓
SARIMA (Seasonal AutoRegressive Integrated Moving Average)
The scenario calls for decomposing a monthly series into a long-term trend and a twelve-month seasonal cycle, then projecting it forward. SARIMA is built precisely for that structure, using seasonal differencing and seasonal AR/MA terms alongside non-seasonal ones. Clustering has no forecasting capability, a CNN is impractical on thirty-six points and not interpretable as trend plus season, and logistic regression targets a categorical outcome rather than continuous volume.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
k-means clustering on the monthly shipment totals
Why it's wrong here
k-means is an unsupervised algorithm that groups similar observations into clusters. It has no notion of time order, trend, or seasonality, so it cannot extrapolate next month's volume from past months. Applying it here might reveal that Decembers resemble each other, but it produces no forecast and ignores the sequential structure that the operations manager explicitly wants to model.
- ✓
SARIMA (Seasonal AutoRegressive Integrated Moving Average)
Why this is correct
SARIMA extends ARIMA with seasonal autoregressive, differencing, and moving average terms, so it models trend and a repeating seasonal cycle simultaneously. With monthly data showing an upward trend and a strong December peak, the seasonal period of twelve captures the yearly pattern while the non-seasonal terms handle the growth. This makes SARIMA the direct match for the manager's stated need to separate trend from seasonality.
- ✗
A convolutional neural network trained on the monthly totals as a 1D signal
Why it's wrong here
A CNN can in principle learn patterns in a 1D series, but with only thirty-six monthly observations it has far too little data to train reliably, and it does not natively decompose trend and seasonality into interpretable components. The manager asked for a method that explicitly separates growth from the yearly cycle, which a CNN does not provide without substantial custom architecture and far more history.
- ✗
Logistic regression using the month number as the predictor
Why it's wrong here
Logistic regression predicts a probability of class membership, so it is suited to binary outcomes such as whether a shipment will be delayed. Shipment volume is a continuous count, making the model type inappropriate. Even if replaced with linear regression, a single month-number predictor would capture only a straight-line trend and could not represent the recurring December peak the manager described.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.