Databricks-DE-Pro Monitoring and Alerting Practice Question
A data engineer is responsible for a Delta Live Tables pipeline that ingests streaming data from multiple sources. The pipeline occasionally experiences delays, and the engineer needs to monitor the pipeline's health. Which two metrics should the engineer monitor to detect ingestion backlog and processing latency? (Choose two.)
⚠ Common exam trap
The trap here is focusing on cost or error metrics rather than the streaming-specific throughput and latency metrics that indicate whether the pipeline is falling behind.
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
✓
The 'processingTime' metric in the streaming query progress
numInputRows and processingTime are key metrics in the streaming query progress that directly reflect ingestion rate and processing duration. Monitoring both allows the engineer to detect when the pipeline is not keeping up with the source, causing backlog and increased latency. Other metrics like error counts, DBU usage, or storage size do not provide the necessary real-time performance insight.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The 'processingTime' metric in the streaming query progress
Why this is correct
processingTime measures how long each micro-batch takes to process. If this value consistently increases, it indicates that the pipeline is unable to keep up with the incoming data rate, leading to increased latency. Monitoring processingTime helps identify performance bottlenecks and potential backlog in Delta Live Tables streaming pipelines.
- ✗
The number of records in the event log with severity 'ERROR'
Why it's wrong here
Error records in the event log indicate failures but do not directly measure ingestion backlog or processing latency. While they are important for troubleshooting, they do not provide the real-time throughput or lag information needed to detect delays in a streaming pipeline. Monitoring errors alone would not reveal gradual performance degradation.
- ✓
The 'numInputRows' metric in the streaming query progress
Why this is correct
numInputRows indicates how many rows were read in each micro-batch. A sudden drop or a consistently low number relative to the source's production rate can signal that the pipeline is falling behind. This metric is part of the streaming query progress and is essential for detecting ingestion backlog and throughput issues in Delta Live Tables.
- ✗
The number of DBUs consumed by the pipeline cluster
Why it's wrong here
DBU consumption reflects cost and resource usage but does not directly indicate ingestion backlog or processing latency. A pipeline could consume many DBUs while still processing data efficiently, or consume few DBUs while falling behind. Therefore, DBU metrics are not suitable for detecting real-time delays in this scenario.
- ✗
The total size of the Delta table in storage
Why it's wrong here
Storage size shows how much data has been written over time but does not reveal whether the pipeline is keeping up with incoming data. A growing table size is expected and does not indicate backlog. This metric is more relevant for capacity planning than for monitoring ingestion latency or throughput in a streaming pipeline.
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.