Databricks-DA-Assoc Importing Data Practice Question
A junior data analyst attempts to load a large JSON file into a Databricks DataFrame using spark.read.json(path), but the resulting DataFrame contains numerous null values across critical columns. Upon inspection, the raw JSON records have inconsistent nesting structures and missing attributes. What is the most effective way for the analyst to inspect the inferred schema before transforming the data?
⚠ Common exam trap
Candidates often try to use 'display()' or 'show()' to inspect schemas. While these show data content, they do not provide the structural metadata or hierarchical types needed to debug schema issues.
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
✓
Run the schema property or printSchema() method on the DataFrame to review the hierarchical structure and data types inferred by Spark.
Using the printSchema() method on the loaded DataFrame prints out the hierarchical data types and inferred structure determined by Spark during the read operation. Data analysts rely heavily on this method to quickly identify schema evolution issues, unexpected nulls, and nested arrays before building downstream analytical queries and dashboards.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Execute the display() function on the raw file path string directly without creating a DataFrame object first.
Why it's wrong here
The display function expects a DataFrame or tabular object as its argument when rendering data in notebooks. Passing a raw file path string directly results in a type error rather than displaying the file contents or schema.
- ✓
Run the schema property or printSchema() method on the DataFrame to review the hierarchical structure and data types inferred by Spark.
Why this is correct
printSchema() displays the inferred schema, including nested struct fields and data types, revealing why inconsistent nesting produced nulls. Reviewing this hierarchy lets the analyst correct the read options or define an explicit schema before transformation.
- ✗
Query the system information tables in the Hive metastore using a standard SQL DESCRIBE DATABASE statement.
Why it's wrong here
The Hive metastore holds metadata only for registered tables and databases, not for an unregistered JSON file read directly from a path, so DESCRIBE DATABASE returns nothing about its inferred schema. It is tempting because DESCRIBE statements do reveal column structures, but only for catalogued objects.
- ✗
Export the JSON file into Microsoft Excel locally to manually count the frequency of null values in each column.
Why it's wrong here
Excel cannot parse nested, inconsistent JSON into a tabular grid, and manual counting gives no access to Spark's inferred schema, which is what the analyst must inspect. It is tempting as a familiar tool for eyeballing small files, yet it fails on large semi-structured data and cannot show struct or array types.
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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.