Databricks-DE-Pro Data Ingestion and Acquisition Practice Question
Exhibit
{"cloudFiles.format": "json", "cloudFiles.schemaLocation": "/tmp/schema", "cloudFiles.inferColumnTypes": "true"}Refer to the exhibit. You are using Auto Loader to ingest data with evolving schemas. After running the job for a week, you realize that new columns added to the source JSON are not being captured in the destination table. What must you add to the configuration?
⚠ Common exam trap
Candidates tend to look for manual ALTER TABLE commands or checkpoint resets, forgetting that Auto Loader needs a specific configuration property for schema evolution.
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
✓
Add 'cloudFiles.schemaEvolutionMode': 'addCol'.
By default, Auto Loader schema inference only detects the schema during the initial load. To capture schema evolution, you must explicitly enable 'cloudFiles.schemaEvolutionMode'. Without this parameter, Auto Loader ignores new fields to protect downstream consumers from breaking changes. Enabling 'addCol' allows the schema to expand dynamically, ensuring the target Delta table reflects the structure of the incoming data files as they arrive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add 'cloudFiles.schemaEvolutionMode': 'rescue'.
Why it's wrong here
The 'rescue' mode is used to capture data that does not match the inferred schema into a special column, not to evolve the schema itself. It helps prevent data loss for malformed records but does not automatically update the table schema to include the new columns found in source files.
- ✓
Add 'cloudFiles.schemaEvolutionMode': 'addCol'.
Why this is correct
Setting schema evolution mode to 'addCol' enables the ingestion process to detect new columns in the source data and automatically add them to the target Delta table. This ensures the table structure remains synchronized with the incoming data stream, preventing the loss of new attributes arriving in source files.
- ✗
Add 'cloudFiles.maxFiles': '1000'.
Why it's wrong here
This parameter controls the maximum number of files to process per trigger, which affects ingestion throughput and latency. It has absolutely no bearing on schema evolution or the detection of new data fields within the source files. Increasing this will not resolve the failure to capture new columns.
- ✗
Add 'cloudFiles.allowOverwrites': 'true'.
Why it's wrong here
Allowing overwrites is used to handle situations where files might be re-uploaded to the same location, which is generally discouraged in streaming pipelines. It does not control schema detection logic or the propagation of new field names from source JSON files into the existing Delta table schema.
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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-Pro 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-Pro exam.