Water-plant turbidity features that were always populated now arrive 40 percent null, and one sensor column flipped from numeric to a status string. Invoke count and latency look fine. Which monitor detects that?
Select an answer to reveal the explanation.
Short Explanation
Turbidity features now arrive 40 percent null, and one column flipped from numeric to a status string. A data-quality monitor catches missingness, type, and range. Invoke count and latency stay green, and model-quality needs labels.
Full Explanation
A data-quality monitor detects input-schema and distribution problems such as missingness, type, and range. Invoke count and latency stay green. Personalize is not that monitor, and model-quality needs labels.