Databricks-DE-Assoc Data Transformation and Modeling Practice Question
Exhibit
{
"operation": "INSERT",
"source_format": "JSON",
"target_table": "raw_data",
"constraint": "NOT NULL",
"status": "FAILED",
"error": "java.lang.NullPointerException: Value at column 'user_id' is null"
}Refer to the exhibit. A data engineer is attempting to ingest JSON data into a Delta table. Based on the error log, what is the most appropriate transformation step to implement before loading this data into the production table?
⚠ Common exam trap
Candidates often attempt to alter the target Delta table configuration to accept bad data rather than cleaning and transforming the incoming stream beforehand.
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 a 'coalesce' function or a 'filter' transformation on the DataFrame before writing to the Delta table.
The exhibit shows a failure due to null values in a required field during ingestion. In production pipelines, failing at the ingestion layer stops data flow. Implementing a transformation step to handle nulls, either by providing default values or filtering, ensures the pipeline is resilient. This is a core Data Engineering principle: handle dirty source data before it reaches the target, preventing ingestion failures.
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 the 'DROP TABLE' command to clear the target and recreate it with nullable constraints.
Why it's wrong here
Dropping a production table is a destructive action that leads to significant data loss and downtime. It is never an appropriate strategy for handling simple schema or constraint violations. Engineers should instead focus on refining the transformation logic to cleanse the incoming data during the ingestion process.
- ✓
Apply a 'coalesce' function or a 'filter' transformation on the DataFrame before writing to the Delta table.
Why this is correct
By applying a 'coalesce' to supply a default value or a 'filter' to remove records with null IDs, the engineer ensures that the data meets the target table's schema requirements. This proactive cleaning prevents the NullPointerException and maintains the integrity of the data stream without failing the job.
- ✗
Modify the target table schema using 'ALTER TABLE' to change the column to nullable.
Why it's wrong here
While altering the table to allow nulls might solve the immediate error, it often violates business logic requirements. If a 'user_id' is mandatory for downstream analysis, allowing nulls creates 'silent failures' in downstream reports, which are significantly harder to debug than an initial ingestion failure.
- ✗
Set the 'spark.sql.execution.nullValue' configuration to 'ignore' for the session.
Why it's wrong here
There is no such Spark configuration that globally ignores null values during write operations. Relying on non-existent or improper configurations leads to unpredictable behavior. Engineers must explicitly define data transformation logic within their code to ensure consistent and predictable handling of null records during the ingestion process.
About these practice questions
Courseiva writes every Databricks-DE-Assoc question from scratch — 276 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.