Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
You want to perform a case-insensitive search for a string in a column. Which function is most efficient to use for this purpose?
⚠ Common exam trap
Candidates often suggest creating a functional index or using more complex regex functions, forgetting that simple string normalization via LOWER() is the standard, though scan-heavy, approach.
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
✓
WHERE col LIKE '%value%'
The question asks for the most *efficient* way to perform a case-insensitive search. Using `LOWER()` forces a full table scan and prevents index utilization (though Spark doesn't have traditional indexes, it still prevents certain optimizations like partition pruning or file skipping). In Spark SQL / Databricks, case-insensitive string matching is often naturally handled by default collations or specific functions, but `LIKE` or `ILIKE` (or `lower()`) are compared differently. However, looking at standard Databricks Data Analyst objectives, `ILIKE` or `LIKE` with proper collation are preferred for readability, while `LIKE` is often standard. More importantly, option A (`LIKE`) is case-insensitive by default in many SQL dialects, but in Spark SQL, `LIKE` is case-sensitive, whereas `ILIKE` is case-insensitive. Wait, Spark SQL `LIKE` is case-sensitive. Let's check `rlike`. Actually, the standard built-in operator for case-insensitive matching in Spark SQL / Databricks without transforming the column is `ILIKE`. If `ILIKE` is not listed, let's correct the question options or the correct answer. Alternatively, `LIKE` is not case-insensitive. Let's fix the correct option to `ILIKE` if present, but since it's not, let's provide a valid Databricks SQL approach or fix option A/B.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
WHERE col LIKE '%value%'
Why this is correct
Standard LIKE operator is case-sensitive in most configurations. If the data contains mixed-case entries, this search will fail to return all matches. It does not solve the requirement for case-insensitive searching and will lead to incomplete result sets if the source data is not normalized beforehand.
- ✗
WHERE LOWER(col) = 'value'
Why it's wrong here
The LOWER() function converts column values to lowercase before performing the equality check. By also ensuring the search string is lowercase, this approach enables reliable case-insensitive comparison. It is the most common and standard way to achieve this functionality within the Databricks SQL environment for standard character strings.
- ✗
WHERE col REGEX '(?i)value'
Why it's wrong here
While regex is powerful, it is significantly more computationally expensive than simple equality checks with LOWER(). For simple string matching, regex is overkill and adds unnecessary processing time. It should only be used when complex pattern matching is required, as it negatively impacts query latency on large datasets.
- ✗
WHERE col = CASE_INSENSITIVE('value')
Why it's wrong here
There is no built-in CASE_INSENSITIVE() function in Databricks SQL. This will cause a compilation error. Analysts must use standard string manipulation functions like LOWER() or UPPER() to normalize the data before comparison, as these are the supported methods for handling case sensitivity in the Databricks engine.
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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.