Databricks-DA-Assoc Importing Data Practice Question
An analyst is loading a CSV file where one column contains values like 007, 012, and 003. After creating a table through the Add Data UI, those values appear as 7, 12, and 3. The analyst needs the leading zeros preserved for downstream reporting. What should the analyst do?
⚠ Common exam trap
The trap here is trusting automatic type inference, which correctly identifies a numeric pattern but destroys leading zeros that matter for identifiers.
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
✓
Specify the column as a string type in the schema settings before creating the table.
Leading zeros carry meaning only in text form, so the column must be declared as a string before the table is created. Letting the reader infer an integer type silently drops the zeros, and no post-load fix reliably restores them. Overriding the type in the schema settings preserves the original values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Specify the column as a string type in the schema settings before creating the table.
Why this is correct
Leading zeros are only meaningful in a string representation. If the column is inferred or declared as an integer, the parser strips the zeros because 007 and 7 are numerically identical. Overriding the inferred type to string preserves the original characters. The Add Data UI and schema editor both allow this type override before the table is created.
- ✗
Apply a formatting function after loading that pads the numbers back to three characters.
Why it's wrong here
Padding after the fact assumes every value should be exactly three characters and that no legitimate shorter values exist, which is fragile. It also means the stored data was already lossy, so any value that was originally longer than three characters would be mishandled. Declaring the column as string at load time avoids this reconstruction guesswork entirely.
- ✗
Enable schema evolution so the column can switch types after the first load.
Why it's wrong here
Schema evolution lets new columns be added or types be widened across loads, but it does not retroactively restore leading zeros that were already stripped during parsing. The loss happens at read time when the value is cast to an integer. Evolution would also not convert an integer column back to string automatically in a way that recovers the zeros.
- ✗
Set the file's delimiter to a character that prevents numeric interpretation of the column.
Why it's wrong here
The delimiter separates fields; it does not influence whether a field is parsed as numeric. Changing the delimiter would break column alignment entirely and still leave the numeric column inferred as an integer. The parsing decision is driven by the column's declared or inferred data type, not by the field separator.
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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.