Databricks-DE-Pro Data Transformation, Cleansing, Quality Practice Question
You need to perform a deduplication task on a streaming source that includes late-arriving data. Which Delta Lake feature is best suited to manage this while ensuring efficient state cleanup?
⚠ Common exam trap
Candidates often select simple batch deduplication methods or manual window operations, failing to recognize that watermarking is essential for managing state and late-arriving data in streaming.
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
✓
Use the dropDuplicates() method with watermark settings to manage state.
The `withWatermark` and `dropDuplicates` combination is specifically designed for streaming deduplication. Watermarks tell the engine how long to wait for late-arriving data, allowing it to clear old state from memory. This is critical for high-volume streaming jobs where state growth would otherwise lead to out-of-memory errors or significant performance degradation, ensuring the job remains stable over long periods of execution.
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 a standard SQL DELETE query with a subquery to identify and remove duplicates.
Why it's wrong here
Standard SQL DELETE operations are designed for batch processing, not streaming. In a streaming context, the table is continuously growing, and manually running a DELETE operation would require a full table scan or complex predicate pushdown, which is inefficient and does not integrate with the streaming state management required for deduplication.
- ✓
Use the dropDuplicates() method with watermark settings to manage state.
Why this is correct
Using dropDuplicates on a streaming DataFrame, combined with a watermark, allows Spark to manage the state of seen records efficiently. The watermark specifies the time limit for which duplicates are tracked, ensuring the state doesn't grow indefinitely, which is essential for long-running streaming pipelines consuming data with late arrivals.
- ✗
Set the table property 'delta.enableChangeDataFeed' to true and filter on the change log.
Why it's wrong here
Change Data Feed (CDF) tracks modifications to the table, but it is not a tool for real-time deduplication of incoming stream batches. While it helps in auditing or incremental ETL, it does not provide the state management mechanisms required to handle streaming duplicates in an automated, performant manner.
- ✗
Increase the 'spark.sql.shuffle.partitions' setting to ensure all duplicates land on the same node.
Why it's wrong here
Changing partition settings might influence data distribution, but it does not provide a mechanism for deduplication. Deduplication requires an understanding of the record's event time and state, which shuffling alone cannot achieve. It is a configuration tuning parameter, not a logic-based solution for data quality problems.
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.