This quiz probes robust techniques to identify and handle extreme values in MMM datasets.
IQR method flags outliers if a data point lies beyond 1.5×IQR above Q3 or below ______.
mean
Q1
mode
median
Winsorising at 99th percentile replaces extreme highs with the value at ______ percentile.
95th
99th
90th
50th
Cook’s distance highlights influential observations combining leverage and ______.
residual size
standard error
cookie
CTR
2025 Robyn defaults to winsorise spends at +/- ______ SD in log space.
1
0.5
2
5
Before winsorising, analysts should understand if spikes are real events or ______ errors.
data
DNS
cookie
creative
MAD (median absolute deviation) thresholding is preferred over z‑score when data are ______.
uniform
non‑normal
censored
perfect
Winsorising revenue but not spend can bias ROI because variance reduction is applied ______.
equally
never
asymmetrically
randomly
Outlier removal logs should capture threshold, count, and ______ of modified points.
dns
pixel
font
IDs
When outliers are frequent, switch to robust regression such as ______ loss.
MAE
MSE
cross‑entropy
Huber
Extreme value theory suggests setting cutoff where tail distribution fits a ______ model.
Gaussian
GPD (generalized pareto)
Poisson
Gamma
Starter
Review the basics.
Solid
Nice work—refine the details.
Expert!
Exceptional command of the topic.
Mastering Outlier Detection & Winsorising Interview Questions means knowing how to spot and adjust extreme values to keep your models reliable. Start your prep with these marketing mix modelling interview questions to see how attribution and mix decisions intersect with data quality. Then explore the MMM dashboard essentials interview guide for tips on crafting clear visualizations. Don’t forget to review the data lakehouse interview resource to understand modern data pipelines. Finally, sharpen your skills with the negative ROI channel detection interview questions to learn how to identify underperforming channels.
