Databricks-DE-Pro Data Modelling Practice Question
Which design pattern is best suited for handling late-arriving data in a medallion architecture?
⚠ Common exam trap
Candidates often suggest using append-only strategies or creating separate tables for late data, which complicates downstream consumption and breaks the integrity of historical reporting.
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
✓
Update the Silver layer using a MERGE operation that matches on event time.
Late-arriving data is common in streaming and batch pipelines. Using a watermarking strategy combined with a merge or upsert operation allows the system to process incoming data while maintaining the integrity of historical windows. By allowing late records to update existing states, the system remains accurate despite delays from the source, which is critical for time-sensitive financial or operational reporting where accuracy is paramount.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Discard all late-arriving records to ensure a clean source-of-truth.
Why it's wrong here
Discarding data leads to inaccurate business metrics, especially in systems where event time is crucial. Data engineering should focus on designing robust pipelines that can handle late arrivals, rather than simply ignoring them, which would compromise the completeness and validity of the final analytical datasets in the Gold layer.
- ✓
Update the Silver layer using a MERGE operation that matches on event time.
Why this is correct
The MERGE operation is ideal for late-arriving data because it can identify existing records and update them with the corrected information. By using event time as a matching key, the pipeline can ensure that even if data arrives out of order, the state of the Silver table remains accurate.
- ✗
Append late-arriving data to the Bronze layer but ignore it in all downstream layers.
Why it's wrong here
Ignoring data in downstream layers means the business never sees the complete picture. This creates a disconnect between the data captured in Bronze and the insights provided in Gold. Effective data modeling requires that late arrivals are integrated into the final reporting tables to ensure consistency across the architecture.
- ✗
Create a separate 'late_data' table and join it to the main table every month.
Why it's wrong here
Creating a separate table adds unnecessary complexity to the model and makes querying significantly harder for end-users. It complicates maintenance and reporting, as users must perform complex joins to see the full dataset. The preferred approach is to integrate late data into the main table structure immediately.
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.