Databricks ML-PRO Lakehouse Monitoring and Drift Guide
April 13, 2026
Study Databricks ML-PRO Lakehouse Monitoring and Drift: key concepts, common traps, and exam decision cues.
On this page
Monitoring questions are where ML-PRO stops rewarding generic MLOps language. Databricks wants the right monitor type, the right metric, and the right response path.
Monitoring map
Requirement
Better first instinct
monitor changing distributions over time
drift metrics in Lakehouse Monitoring
monitor predictions over time
inference table plus monitoring workflow
choose between snapshot, time-series, or inference monitoring
table type should match the use case
notify stakeholders when thresholds are exceeded
alerting tied to defined metrics and thresholds
What the exam is really testing
If the stem says…
Strong reading
“data table type”
monitor design depends on table role
“detect drift in numerical or categorical data”
choose relevant metrics and comparison baseline
“model performance trends over time”
inference monitoring path matters
“infrastructure metrics such as latency and error rate”
distinguish endpoint health from model-quality drift
Decision order that usually wins
Identify whether the problem is data drift, model quality, or serving health.
Choose the monitored table type that matches the use case.
Pick metrics and baselines that actually reveal the suspected failure mode.
Set alert thresholds that map to a concrete operational response.
Route the alert toward retrain, rollback, investigation, or infrastructure repair deliberately.
Monitoring questions are mostly classification questions. The strongest answer usually wins by naming the right signal lane before choosing a metric or an alert.
Scenario triage
Scenario
Better first move
feature distributions are drifting over time
use drift metrics in Lakehouse Monitoring
live prediction quality or inference outputs need observation
use inference monitoring path
request latency and error rate degrade
treat it as serving health first
team wants alerts without an action plan
define the response path before tuning thresholds
Common traps
Trap
Better rule
treating every issue as a model-quality problem
some are endpoint-health or data-quality problems
using one monitor type for every use case
table type should match the monitoring need
firing alerts without a defined action path
alerting should lead to retrain, rollback, block, or investigation