- A
Add a binary feature indicating whether each quarter was during the pandemic.
Why wrong: Adding a feature helps the model distinguish but still includes the anomalous data; the model may still overfit to that period.
- B
Remove the data points corresponding to the pandemic year from the training set.
Removing the outlier helps the model focus on typical patterns, improving generalization to future non-pandemic quarters.
- C
Normalize the entire dataset using Z-scores to reduce the impact of the outlier.
Why wrong: Normalization reduces but does not eliminate the outlier's influence; the model may still be skewed.
- D
Include the outlier data and increase the model capacity to capture the anomaly.
Why wrong: Increasing capacity may lead to overfitting on the anomaly, causing continued overestimation.
Quick Answer
The answer is to remove the data points corresponding to the pandemic year from the training set. This is correct because the pandemic year represents a non-recurring outlier that distorts the model’s ability to learn the true seasonal pattern in the historical data; when handling outliers in training data for revenue forecasting, including such an extreme anomaly forces the model to overcompensate, leading to consistent overestimation of future revenue. On the Salesforce AI Associate exam, this scenario tests your understanding of data preparation and the impact of outliers on model generalization—a common trap is assuming more data always improves accuracy, but here the outlier is not representative of future quarters. Remember the memory tip: “Outliers that don’t repeat should be deleted.”
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 sales operations team is training an AI model to forecast quarterly revenue. They have five years of historical data, which includes a strong seasonal pattern but also a significant outlier: during the pandemic year, revenue dropped by 70% from typical values. The model trains with high accuracy on historical data but fails to predict future quarters accurately, consistently overestimating revenue. What should the data scientist do to improve forecast 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
Remove the data points corresponding to the pandemic year from the training set.
Option B is correct because removing the pandemic year data eliminates the extreme outlier that is causing the model to learn a distorted seasonal pattern. The 70% revenue drop is not representative of future quarters, so including it forces the model to overestimate revenue to compensate for the anomaly. By training only on typical data, the model can learn the true seasonal pattern and generalize better to future quarters.
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 binary feature indicating whether each quarter was during the pandemic.
Why it's wrong here
Adding a feature helps the model distinguish but still includes the anomalous data; the model may still overfit to that period.
- ✓
Remove the data points corresponding to the pandemic year from the training set.
Why this is correct
Removing the outlier helps the model focus on typical patterns, improving generalization to future non-pandemic quarters.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Normalize the entire dataset using Z-scores to reduce the impact of the outlier.
Why it's wrong here
Normalization reduces but does not eliminate the outlier's influence; the model may still be skewed.
- ✗
Include the outlier data and increase the model capacity to capture the anomaly.
Why it's wrong here
Increasing capacity may lead to overfitting on the anomaly, causing continued overestimation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that you should keep all data and adjust the model (e.g., via normalization or capacity increase) rather than removing non-representative outliers, leading candidates to pick options like C or D.
Detailed technical explanation
How to think about this question
Under the hood, time-series forecasting models like ARIMA or LSTMs rely on learning stable seasonal components and trends. A 70% drop introduces a leverage point that shifts the model's learned intercept or weights disproportionately, especially in gradient-based optimization where squared error loss amplifies the outlier's influence. In practice, data scientists often use robust statistics (e.g., median-based methods) or explicitly remove anomalous periods to preserve the stationarity assumption required for accurate forecasting.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove the data points corresponding to the pandemic year from the training set. — Option B is correct because removing the pandemic year data eliminates the extreme outlier that is causing the model to learn a distorted seasonal pattern. The 70% revenue drop is not representative of future quarters, so including it forces the model to overestimate revenue to compensate for the anomaly. By training only on typical data, the model can learn the true seasonal pattern and generalize better to future quarters.
What should I do if I get this AI Associate 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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Last reviewed: Jun 30, 2026
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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