Databricks-DE-Pro Monitoring and Alerting Practice Question
A data engineer wants to monitor the health of a Delta Live Tables pipeline and receive alerts when the pipeline fails to meet its data quality expectations. The pipeline has several expectations defined. Which Databricks feature should the engineer use to set up these alerts?
⚠ Common exam trap
The trap here is assuming that job notifications or cluster metrics can alert on data quality expectations, when only the DLT event log contains that granular information.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Delta Live Tables event log with Databricks SQL alerts.
The Delta Live Tables event log captures detailed data quality metrics and expectation results. By using Databricks SQL alerts to query this log, engineers can set up precise alerts for data quality violations. Other options either lack the necessary data quality context or only provide high-level failure notifications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Job notifications configured on the DLT pipeline's underlying job.
Why it's wrong here
Job notifications can alert on pipeline failure, but they do not provide granular alerts on individual data quality expectations. The requirement is to be alerted when the pipeline fails to meet data quality expectations, which requires access to expectation metrics. Job notifications alone are insufficient.
- ✗
Databricks SQL dashboard with a query on the target Delta table.
Why it's wrong here
A SQL dashboard can visualize data in the target table, but it does not capture DLT expectation results or pipeline health metrics. It would only show the final data, not the quality checks. This approach lacks the necessary context to alert on data quality expectations.
- ✓
Delta Live Tables event log with Databricks SQL alerts.
Why this is correct
The Delta Live Tables event log records data quality metrics and expectation outcomes. By querying the event log with Databricks SQL alerts, engineers can set up notifications when expectations fail or metrics drop below thresholds. This is the native and most integrated way to monitor DLT pipeline health and data quality.
- ✗
Cluster metrics in the Databricks workspace.
Why it's wrong here
Cluster metrics provide information about resource utilization, such as CPU and memory, but they do not include data quality metrics or expectation outcomes. They are useful for performance monitoring but cannot alert on data quality failures specific to DLT expectations.
About these practice questions
Courseiva writes every Databricks-DE-Pro question from scratch — 267 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.