Databricks-DA-Assoc Importing Data Practice Question
Which THREE actions occur when using Auto Loader with schema evolution enabled? (Choose three)
⚠ Common exam trap
Candidates mistakenly believe Auto Loader automatically deletes unparseable files or alters original source files, when it actually captures corrupted data in a rescued data column.
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
✓
New columns found in the source files are automatically added to the target table.
Schema evolution is a powerful feature of Auto Loader that allows pipelines to adapt to changes in upstream data without requiring manual code updates. By understanding how the schema is managed, detected, and reconciled, analysts can build flexible ingestion systems that reduce maintenance overhead. This is critical in modern data environments where upstream sources frequently add or rename columns, requiring the platform to dynamically update downstream tables to accommodate the new structure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
New columns found in the source files are automatically added to the target table.
Why this is correct
Auto Loader detects new columns in source data and can automatically update the target table's schema. This prevents pipeline failure when source data expands, allowing the table to evolve alongside the data, which is a key advantage of the Delta Lake schema evolution capabilities provided during ingestion.
- ✗
Existing columns with data type conflicts cause the pipeline to stop immediately.
Why it's wrong here
While data type conflicts can cause issues, Auto Loader with schema evolution enabled is designed to handle discrepancies gracefully. It does not automatically stop unless specifically configured to do so, preferring to either capture the data in a hidden column or attempt to resolve the type based on settings.
- ✗
The schema is inferred from the metadata of the cloud storage provider.
Why it's wrong here
Schema inference is performed by sampling the actual data within the files, not by querying cloud metadata. Cloud storage providers do not hold internal schema definitions for text-based files like CSV or JSON, so Auto Loader must inspect the file content itself to determine the data structure during the process.
- ✓
A 'rescued data' column is created to store data that does not match the inferred schema.
Why this is correct
The rescued data column is a standard feature in Auto Loader that ensures no data is lost when incoming records do not fit the established schema. This allows analysts to inspect the data later, fix schema issues, and recover the records without halting the entire streaming pipeline during ingestion operations.
- ✓
The inferred schema is saved to a checkpoint location to maintain consistency across restarts.
Why this is correct
Auto Loader saves the inferred schema to the checkpoint directory. This ensures that when the stream restarts, it continues using the established schema instead of re-inferring it, which maintains consistency and prevents unexpected changes in the data structure between different execution runs of the same ingestion pipeline.
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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-DA-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-DA-Assoc exam.