Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer wants to move data from a 'Bronze' table to a 'Silver' table while performing data cleaning. They want to ensure that this process is only executed once per data batch. Which approach is best for this requirement?
⚠ Common exam trap
Candidates often confuse batch operations like standard DataFrame reads and writes with streaming sources, forgetting that batch reads do not inherently guarantee exactly-once state management.
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 Structured Streaming with Delta as source and sink
Using Spark Structured Streaming with a Delta Lake source and sink provides built-in exactly-once processing guarantees. The streaming engine manages offsets and state, ensuring that every record is processed exactly once even in the event of a failure. This is the recommended pattern for moving data across the Medallion architecture, providing a robust, fault-tolerant way to implement complex data transformations and business logic consistently.
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 daily batch job with a 'WHERE' clause on timestamp
Why it's wrong here
Batch jobs based on timestamps are prone to issues like missing records if a job fails during the middle of an ingestion window. It requires manual handling of state and watermark logic, which is far less reliable than the built-in state management provided by structured streaming for incremental data processing.
- ✓
Use Structured Streaming with Delta as source and sink
Why this is correct
Structured Streaming provides native exactly-once semantics by tracking offsets in checkpoints. This allows the system to automatically resume from the exact point of failure, ensuring no data is processed twice or lost. This is the standard, highly resilient approach for implementing incremental transformations between Medallion architecture layers in modern data engineering.
- ✗
Use a simple 'INSERT OVERWRITE' every hour
Why it's wrong here
INSERT OVERWRITE replaces the entire table or partition. If the intention is to append data to the Silver layer, this would result in massive data loss unless the entire source is reprocessed every time. It is not an incremental processing approach and is highly inefficient for large-scale data transformation workflows.
- ✗
Use a manual Python script to fetch data and write it back
Why it's wrong here
Manual scripts lack the necessary fault-tolerance, state management, and scalability features required for production pipelines. They are difficult to monitor, debug, and optimize. Leveraging Spark's native streaming capabilities is essential for ensuring that transformations are handled reliably without the fragility associated with custom-coded ingestion scripts that lack built-in retry logic.
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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.