Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A junior data engineer writes a PySpark transformation that reads a Parquet dataset, filters out inactive users, and appends the resulting DataFrame to an existing Delta Lake table. However, the data engineer notices duplicate records appearing in the target table after multiple runs. Which technique should be implemented to ensure idempotency?
⚠ Common exam trap
Candidates often try to solve duplicates using 'overwrite' modes or manual deduplication steps, failing to realize that Delta Lake's MERGE command is the native, idempotent solution for upserting 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 Delta Lake MERGE operation using a unique identifier to conditionally insert or update records.
Using Delta Lake's MERGE INTO operation allows data engineers to perform upserts by matching incoming records against existing table rows using a unique business key. This prevents duplicates during repeated pipeline executions, guaranteeing idempotency. Traditional append operations simply add records without checking for prior existence, which is a common root cause of duplicate data issues in incremental batch pipelines.
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 DataFrameWriter's mode("overwrite") with replaceWhere option matching the updated partition keys.
Why it's wrong here
While overwrite mode with replaceWhere replaces partition data cleanly, it operates at the partition level rather than the individual record level. If multiple updates occur within the same partition without touching all records, partition-level replacement might inadvertently drop unselected rows or fail to deduplicate incremental row appends.
- ✓
Implement a Delta Lake MERGE operation using a unique identifier to conditionally insert or update records.
Why this is correct
Delta Lake MERGE provides a robust mechanism to perform atomic upserts based on matching keys. By checking whether a record already exists before inserting or updating, pipelines can be executed repeatedly with identical source data without introducing duplicate rows, thereby establishing pipeline idempotency.
- ✗
Increase the shuffle partition count using spark.sql("SET spark.sql.shuffle.partitions = 200").
Why it's wrong here
Adjusting shuffle partition settings changes how data tasks are distributed across executor nodes during aggregation or join operations. It has no effect on record deduplication, table state validation, or preventing duplicate writes caused by appending unverified batch data.
- ✗
Execute the VACUUM command immediately after every write operation completes.
Why it's wrong here
The VACUUM command removes physical data files older than a specified retention threshold from cloud storage to reclaim space. It does not prevent duplicate record insertion during write operations and cannot be used to deduplicate active rows in a Delta table.
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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-Assoc 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-Assoc exam.