Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer is designing a Bronze-to-Silver transformation pipeline using Delta Lake. They need to ensure that the Silver table contains only records where the 'transaction_id' is not null and the 'amount' is positive. Which technique best ensures data quality at this stage?
⚠ Common exam trap
Candidates often suggest using complex post-write cleanup jobs or external scripts, ignoring that filtering data during the streaming write is the most efficient and standard practice.
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 a filter transformation in the Spark Structured Streaming query before writing to the target table.
Implementing Delta Lake expectations or inline 'where' clauses during the write process is critical for maintaining high-quality Silver layers. By filtering records before they are committed, you prevent corrupt data from propagating to downstream analytical tables. This pattern is fundamental to the Medallion Architecture, ensuring the Silver layer serves as a reliable, cleaned source of truth for downstream consumption and complex modeling tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a post-load DELETE statement to the Silver table after every micro-batch.
Why it's wrong here
Post-load DELETE statements are inefficient because they require scanning and rewriting entire Parquet files in the Delta table. This approach causes unnecessary write amplification and latency, especially as the table grows. It is better to filter the data stream before the write operation occurs to maintain optimal performance.
- ✗
Define a separate table for rejected records and use manual SQL queries to move them later.
Why it's wrong here
While maintaining a quarantine table is a valid strategy for auditing, it does not solve the requirement of ensuring the Silver table itself contains only valid data. Manual SQL intervention introduces human error and breaks the automated nature of production pipelines, which should be self-healing and fully declarative.
- ✓
Use a filter transformation in the Spark Structured Streaming query before writing to the target table.
Why this is correct
Filtering during the stream transformation ensures that invalid data is dropped before the commit happens. This is the most efficient method because it eliminates invalid records without needing extra write operations. It keeps the Silver table clean from the beginning, adhering to best practices for production ETL pipelines.
- ✗
Set the table property 'delta.constraints.check' to filter incoming null values.
Why it's wrong here
While Delta table constraints can enforce data integrity, they are designed to fail writes if a record violates the constraint, not to automatically filter them out. If an invalid record arrives, the entire batch would fail, stopping the pipeline instead of processing the valid records as requested by 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-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.