Databricks-DE-Pro Monitoring and Alerting Practice Question
A Data Engineer needs to monitor the health of a Delta Live Tables (DLT) pipeline. Which metric should they monitor to track the number of data quality violations over time?
⚠ Common exam trap
Candidates often confuse general pipeline execution logs with specific data quality tracking metrics like expectations_violated_records when monitoring validation issues.
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
✓
expectations_violated_records
The 'expectations_violated_records' metric is specifically designed to track data quality issues in DLT pipelines. By integrating these metrics with Databricks SQL alerts or external tools like Grafana, engineers can gain visibility into data lineage and quality degradation. Monitoring these metrics is essential for maintaining pipeline reliability and ensuring that downstream data consumers receive accurate and validated data, preventing the propagation of corrupted data throughout the analytical ecosystem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
pipeline_latency_seconds
Why it's wrong here
This metric tracks the time taken for data to move through the pipeline. While important for performance monitoring and identifying bottlenecks, it does not provide insights into data quality or the number of records that failed validation checks according to defined pipeline expectations.
- ✓
expectations_violated_records
Why this is correct
This metric exposes the number of records that failed a quality expectation constraint during the execution of a DLT pipeline. Tracking this count allows engineers to identify data drift or schema issues, enabling proactive remediation and ensuring that final tables meet established business quality standards.
- ✗
cluster_cpu_utilization
Why it's wrong here
This metric measures hardware resource consumption by the underlying compute cluster. While it is vital for cost optimization and identifying scaling needs, it provides no visibility into the actual data content, quality, or whether specific records violated schema or business logic constraints.
- ✗
task_retry_count
Why it's wrong here
This metric tracks how many times a pipeline task has attempted to restart after a failure. While high retry counts indicate infrastructure or logic issues, this metric does not indicate data quality failures, which often occur during successful task runs without triggering pipeline retries.
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.