Databricks-DE-Assoc Data Ingestion and Loading Practice Question
Exhibit
{
"cloudFiles.format": "json",
"cloudFiles.schemaLocation": "/mnt/bronze/schemas/orders",
"cloudFiles.inferColumnTypes": "true",
"cloudFiles.schemaEvolutionMode": "addNewColumns"
}Refer to the exhibit. A data engineer is configuring an Auto Loader stream with the provided options. What will happen if a new JSON file arrives containing a field that is not currently in the target table schema?
⚠ Common exam trap
Candidates often assume schema evolution requires manual table alterations or that it will crash the stream, failing to realize that Auto Loader can dynamically update the metastore schema automatically when configured correctly.
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
✓
The new field will be added to the target table automatically.
The schemaEvolutionMode configuration determines how Auto Loader reacts to changes in the source data structure. By setting this to addNewColumns, the engineer ensures that the pipeline remains operational when new fields appear. This is a key part of the Medallion architecture where bronze tables must capture all incoming raw data without strict validation rules.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The stream will fail and require a manual schema update.
Why it's wrong here
This behavior would occur if the schema evolution mode was not specified or if it was set to fail on schema mismatch. However, the current configuration explicitly allows for the addition of new columns, preventing the stream from failing and allowing for continuous data processing without manual developer intervention.
- ✓
The new field will be added to the target table automatically.
Why this is correct
The addNewColumns mode enables Auto Loader to update the table schema dynamically. When a new column is detected in the source files, it is added to the table's metadata and the data is successfully ingested. This allows the pipeline to adapt to upstream changes without stopping or losing data.
- ✗
The new field will be dropped and only existing columns are kept.
Why it's wrong here
Dropping unknown columns is characteristic of standard Spark static reads or certain configurations of the rescued data column. In this specific setup, the configuration is set to evolve the schema, which means the metadata will be updated to include the new field rather than ignoring the incoming data.
- ✗
The data for the new field will be moved to a _rescued_data column.
Why it's wrong here
While the rescued data column is used to store data that doesn't match the expected type, it is not the primary mechanism for adding new columns when addNewColumns is enabled. The new field will be promoted to its own column in the table, rather than being sequestered in the rescue column.
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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.