Databricks-DE-Pro Data Ingestion and Acquisition Practice Question
When ingesting data from a Kafka topic into Delta Lake, what is the best way to handle out-of-order data arriving in the stream?
⚠ Common exam trap
Candidates often propose using windowing functions or sorting the entire dataframe, which are inefficient and do not correctly handle the state management required for streaming late-arriving data.
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
✓
Implement a watermark on the event time column.
In streaming systems, data can arrive delayed. Using Watermarking allows the system to define a threshold for how long it will wait for late data. By keeping state for the specified duration, the engine can correctly aggregate or join records that arrived out of order. This is a standard and essential technique in Spark Structured Streaming to ensure the accuracy of time-windowed operations in high-throughput environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable all aggregations to avoid processing errors.
Why it's wrong here
Disabling aggregations to avoid out-of-order issues is an unnecessary limitation that sacrifices the analytical value of the pipeline. Watermarking provides a robust way to handle lateness without needing to abandon core functionality like windowing or aggregations. You should always aim to handle data late arrival rather than avoiding it.
- ✓
Implement a watermark on the event time column.
Why this is correct
Watermarking allows you to specify the maximum threshold for data lateness. Records arriving within this window are processed correctly, while data arriving later is dropped. This mechanism provides a mathematically sound way to balance correctness and system memory usage, effectively handling out-of-order data streams in a distributed environment.
- ✗
Increase the 'spark.sql.shuffle.partitions' to 5000.
Why it's wrong here
Shuffle partitions control the degree of parallelism for wide transformations but do not address the logical problem of out-of-order event timestamps. Increasing partitions might improve performance for large aggregations, but it will not help the streaming engine understand which events are late or how to order them correctly.
- ✗
Use the 'Trigger.Once' execution mode.
Why it's wrong here
Trigger.Once is an execution mode for batch-like processing, not for handling continuous streaming data with complex temporal requirements. It does not provide the stateful processing capability required to manage out-of-order events. While it processes data in batches, it does not solve the underlying ordering problem inherent in late-arriving events.
About these practice questions
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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.