Databricks-GenAI-Assoc Data Preparation Practice Question
You are building a data preparation pipeline for a generative AI application. The raw data is stored in a Unity Catalog volume as JSON files with nested fields. You need to flatten the nested structure and extract specific fields into a Delta table for downstream embedding. The JSON schema may evolve, with new fields added occasionally. Which approach provides the most robust and maintainable solution?
⚠ Common exam trap
The trap here is assuming that inferring a schema at runtime solves schema evolution, when only a flexible type like variant truly accommodates new fields without pipeline changes.
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 the variant data type to store the JSON and then use variant_get to extract fields.
The variant data type stores JSON without a rigid schema, and variant_get extracts fields dynamically, accommodating schema evolution. This avoids brittle predefined schemas and manual updates. Other options either require static schemas, rely on sampling, or only handle arrays, making them less robust for evolving nested JSON.
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 explode function on the nested arrays and then select the required fields.
Why it's wrong here
explode is used to flatten arrays into rows, but it does not handle nested structs or schema evolution. It requires prior knowledge of the array structure and does not extract fields from nested objects. This approach is limited and would not provide a robust solution for evolving JSON with nested fields.
- ✗
Use the from_json function with a predefined schema and select the required fields.
Why it's wrong here
A predefined schema with from_json will fail or drop new fields when the JSON schema evolves. It requires manual schema updates whenever new fields appear, which is not maintainable. While it provides structure, it is brittle in the face of schema evolution, making it unsuitable for a pipeline that must handle occasional new fields.
- ✗
Use the schema_of_json function to infer the schema at runtime and then apply from_json.
Why it's wrong here
schema_of_json infers a schema from a sample, but it can be inconsistent and may not capture all fields if the sample is not representative. It also does not automatically handle schema evolution across files; the inferred schema is static for the query. This approach adds complexity and may still fail when new fields appear in later data.
- ✓
Use the variant data type to store the JSON and then use variant_get to extract fields.
Why this is correct
The variant data type in Databricks can store semi-structured JSON without a predefined schema, and variant_get allows extracting fields by path. This handles schema evolution gracefully because new fields are automatically included in the variant. It is a robust, maintainable solution for evolving JSON, and it integrates with Delta tables for downstream processing.
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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-GenAI-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-GenAI-Assoc exam.