Databricks-DE-Pro Data Transformation, Cleansing, Quality Practice Question
A Data Engineer is using Delta Live Tables to process a streaming source that contains duplicate records based on an 'event_id'. The engineer needs to ensure that only the latest record for each 'event_id' is retained in the target table, and the pipeline should handle late-arriving data. Which DLT feature should be used?
⚠ Common exam trap
The trap here is assuming that a simple MERGE or aggregation can handle late-arriving data; only the APPLY CHANGES API provides native sequencing and deduplication for streams.
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
✓
Apply changes with deduplication using the APPLY CHANGES API with a sequence column
The APPLY CHANGES API in DLT is designed for change data capture and deduplication. By specifying a key column and a sequence column, it ensures that only the latest record for each key is retained, even with late-arriving data. This declarative approach simplifies pipeline code and leverages DLT's built-in handling of streaming data and state management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Delta Lake's MERGE INTO with a window function to rank records
Why it's wrong here
While MERGE INTO can be used for deduplication, it is not a DLT feature and requires manual orchestration. It does not automatically handle late-arriving data or provide the declarative semantics of DLT's APPLY CHANGES API, which is purpose-built for this scenario with minimal code and built-in handling of out-of-order events.
- ✗
Create a materialized view with a GROUP BY event_id and MAX(timestamp)
Why it's wrong here
A materialized view with aggregation would collapse records but does not handle streaming ingestion natively or late-arriving data. It also does not support incremental updates as new data arrives, making it unsuitable for a streaming pipeline that must continuously deduplicate based on the latest record.
- ✗
Use a streaming live table with a foreachBatch function to manually deduplicate
Why it's wrong here
While foreachBatch allows custom logic, it does not provide built-in deduplication or late-arriving data handling. It would require complex manual state management and is not the recommended DLT feature for this use case, as it lacks the declarative simplicity and reliability of the APPLY CHANGES API.
- ✓
Apply changes with deduplication using the APPLY CHANGES API with a sequence column
Why this is correct
The APPLY CHANGES API (formerly APPLY CHANGES INTO) supports deduplication by specifying a sequence column (e.g., event timestamp) and a key (event_id). It ensures only the latest record per key is retained, handling late-arriving data by ordering on the sequence column. This directly meets the requirement.
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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.