AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A retail company uses a machine learning model to forecast daily product demand. The model is a time series model that uses historical sales data. The model has been performing well, but recently the forecasts have been consistently too low, leading to stockouts. The data scientist notices that the model was trained on data up to last year, and the company has since launched a successful marketing campaign that increased sales by 20%. The data scientist needs to update the model to reflect the new sales patterns. Which approach should the data scientist take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrain the model on the most recent data that includes the sales from the marketing campaign.
Option D is correct because retraining the model on the most recent data that includes the sales from the marketing campaign allows the model to learn the new underlying pattern in the time series. Since the model is a time series model, it relies on historical patterns to make forecasts; retraining on data that captures the 20% sales lift ensures the model adapts to the new demand level, reducing the persistent underforecasting.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Add a feature for the marketing campaign and continue using the old model.
Why it's wrong here
The old model was not trained with this feature, so it won't be used effectively without retraining.
✗
Switch to a different model type, such as ARIMA, without retraining.
Why it's wrong here
ARIMA also requires retraining on recent data to capture the new pattern.
✗
Multiply the model's predictions by 1.2 to account for the marketing campaign.
Why it's wrong here
This is a static adjustment that does not adapt to future changes and may introduce errors.
✓
Retrain the model on the most recent data that includes the sales from the marketing campaign.
Why this is correct
Retraining on recent data allows the model to learn the new sales pattern and improve forecasts.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think a simple multiplicative adjustment (Option C) is sufficient, but Cisco tests the understanding that time series models must be retrained on the new distribution to maintain forecast accuracy, as static adjustments ignore changes in the underlying data-generating process.
Detailed technical explanation
How to think about this question
Time series models such as ARIMA, Prophet, or LSTM capture temporal dependencies through learned parameters (e.g., autoregressive coefficients, seasonal components). Retraining on recent data allows the model to adjust these parameters to reflect the new mean level and potential changes in variance or seasonality. In practice, a marketing campaign might also alter the day-of-week effects or holiday patterns, which a simple scaling factor cannot capture.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Retrain the model on the most recent data that includes the sales from the marketing campaign. — Option D is correct because retraining the model on the most recent data that includes the sales from the marketing campaign allows the model to learn the new underlying pattern in the time series. Since the model is a time series model, it relies on historical patterns to make forecasts; retraining on data that captures the 20% sales lift ensures the model adapts to the new demand level, reducing the persistent underforecasting.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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