DY0-001 · Question #53
A data scientist needs to: - Build a predictive model that gives the likelihood that a car will get a flat tire. - Provide a data set of cars that had flat tires and cars that did not. All the cars…
The correct answer is D. Lagged observations. Because tire-pressure sensors report only weekly measurements, you risk missing the critical pressure drop immediately preceding a flat. Those stale ("lagged") readings may not reflect the condition just before failure, undermining your model's ability to learn the true…
Question
A data scientist needs to:
- Build a predictive model that gives the likelihood that a car will
get a flat tire.
- Provide a data set of cars that had flat tires and cars that did not.
All the cars in the data set had sensors taking weekly measurements of tire pressure similar to the sensors that will be installed in the cars consumers drive. Which of the following is the most immediate data concern?
Options
- AGranularity misalignment
- BMultivariate outliers
- CInsufficient domain expertise
- DLagged observations
How the community answered
(43 responses)- A12% (5)
- B5% (2)
- C28% (12)
- D56% (24)
Explanation
Because tire-pressure sensors report only weekly measurements, you risk missing the critical pressure drop immediately preceding a flat. Those stale ("lagged") readings may not reflect the condition just before failure, undermining your model's ability to learn the true precursors to a flat
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