When monitoring model performance trends over time using an inference table, what is the primary benefit of computing metrics at regular time intervals?

It eliminates the need for ground truth labels

It allows detection of performance degradation over time

It automatically retrains the model when performance drops

It reduces the storage requirements of the inference table

A team deploys a model behind a REST endpoint. They observe that the 95th percentile latency has increased from 200ms to 800ms over the past week, while the request rate has remained stable. What does this indicate?

The model is producing more accurate predictions.

The endpoint is healthy because the request rate is stable.

The input data distribution has changed.

The endpoint may be experiencing performance degradation, such as increased resource contention or inefficient code.

DatabricksCertified Machine Learning Professional

Domain 2Objective 5

Drift Detection and Lakehouse Monitoring MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 4)

Part of the ML Ops domain, which accounts for 44% of the MACHINE-LEARNING-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~22–35 in this domain), expect 4–7 from this objective — we provide 34 practice questions to prepare you well beyond it. (estimate)

34questions here

7free pages

10concepts

44%of the exam

Questions 16–20

16foundation · easy
When monitoring model performance trends over time using an inference table, what is the primary benefit of computing metrics at regular time intervals?
Select an answer first
17foundation · easy
A data scientist wants to monitor a table that stores a snapshot of customer features taken at a single point in time. Which Lakehouse Monitoring table type should be used?
Select an answer first
18foundation · easy
Which Lakehouse Monitoring table type is specifically designed to store model predictions along with ground truth labels for performance monitoring?
Select an answer first
19foundation · easy
Which metric is commonly used to monitor the responsiveness of a model endpoint?
Select an answer first
20application · medium
A team deploys a model behind a REST endpoint. They observe that the 95th percentile latency has increased from 200ms to 800ms over the past week, while the request rate has remained stable. What does this indicate?
Select an answer first

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Drift Detection and Lakehouse Monitoring MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 4)

Questions 16–20

When monitoring model performance trends over time using an inference table, what is the primary benefit of computing metrics at regular time intervals?

A data scientist wants to monitor a table that stores a snapshot of customer features taken at a single point in time. Which Lakehouse Monitoring table type should be used?

Which Lakehouse Monitoring table type is specifically designed to store model predictions along with ground truth labels for performance monitoring?

Which metric is commonly used to monitor the responsiveness of a model endpoint?

A team deploys a model behind a REST endpoint. They observe that the 95th percentile latency has increased from 200ms to 800ms over the past week, while the request rate has remained stable. What does this indicate?

Keep studying — ML Ops