Databricks Certified Data Engineer - Associate

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A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.

The user_ltv table has the following schema: email STRING, age INT, ltv INT

The following view definition is executed:

CREATE VIEW user_ltv_no_minors AS
SELECT email, age, ltv
FROM user_ltv
WHERE
    CASE
        WHEN is_member("auditing") THEN TRUE
        ELSE age >= 18
    END

CREATE VIEW user_ltv_no_minors AS
SELECT email, age, ltv
FROM user_ltv
WHERE
    CASE
        WHEN is_member("auditing") THEN TRUE
        ELSE age >= 18
    END

An analyst who is not a member of the auditing group executes the following query:

SELECT * FROM user_ltv_no_minors

SELECT * FROM user_ltv_no_minors

Which statement describes the results returned by this query?

Real Exam

Community

KKeng

Last updated: January 23, 2026 at 10:45

All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

All age values less than 18 will be returned as null values, all other columns will be returned with the values in user_ltv.

All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.

All records from all columns will be displayed with the values in user_ltv.

All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

Explanation:

Why the correct answer is A

The WHERE clause logic is:

CASE
  WHEN is_member("auditing") THEN TRUE
  ELSE age >= 18
END

CASE
  WHEN is_member("auditing") THEN TRUE
  ELSE age >= 18
END

The analyst is not in the auditing group
Therefore, the filter age >= 18 is applied
Mathematically, age >= 18 is exactly the same as “age greater than 17”

So the query returns:

✅ All columns (email, age, ltv)
✅ Only rows where age >= 18
❌ Rows with age < 18 are completely omitted

This is precisely what option A states.

❌ Why other options are incorrect

B & C: Rows are filtered out, not masked with NULL
D: Not all records are returned
E: Incorrect wording — “greater than 18” excludes age 18

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