If a sensor reports an outlier value that conflicts with corroborating data, what is the recommended approach?

If a sensor reports an outlier value that conflicts with corroborating data, what is the recommended approach?

• A. Investigate and verify sources before acting.
• B. Automatically engage based on the outlier.
• C. Discard the data as unreliable.
• D. Permanently disable the sensor.

Answer: (C)

Handling outlier data requires prioritizing reliability. When a sensor produces a value that doesn’t align with corroborating measurements, treat that reading as potentially faulty and remove it from the current analysis rather than guiding actions with it. This keeps decisions based on trusted information and avoids responding to a single suspect value. After discarding, you can verify the sensor and the related data streams, confirm the integrity of the corroborating data, and re-evaluate with a clean dataset. Acting automatically on the outlier is risky because it could trigger inappropriate actions, and permanently disabling the sensor is an excessive step unless persistent, verified faults exist across multiple checks.

Handling outlier data requires prioritizing reliability. When a sensor produces a value that doesn’t align with corroborating measurements, treat that reading as potentially faulty and remove it from the current analysis rather than guiding actions with it. This keeps decisions based on trusted information and avoids responding to a single suspect value. After discarding, you can verify the sensor and the related data streams, confirm the integrity of the corroborating data, and re-evaluate with a clean dataset. Acting automatically on the outlier is risky because it could trigger inappropriate actions, and permanently disabling the sensor is an excessive step unless persistent, verified faults exist across multiple checks.