PDE Preparing and Using Data for Analysis Practice Question
A company uses Dataplex to manage data quality across multiple BigQuery datasets. They want to define a data quality rule that checks if a column 'email' contains a valid email format. Which Dataplex feature should they use?
⚠ Common exam trap
PDE often tests the assumption that Dataplex ships a rich library of semantic built-in rules (email, phone, SSN); in reality only generic rule types exist, so pattern validation must be expressed as a regex rule.
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
✓
Create a custom Data Quality rule using the 'regex' type.
Dataplex Data Quality tasks support a set of built-in rule types (range, non-null, uniqueness, set, regex, sql_assertion, row_condition), and email format validation is not one of the built-in types. The 'regex' rule type lets you supply a regular expression that each value in the 'email' column must match, so a pattern like ^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$ enforces valid email formatting. This is the intended Dataplex mechanism for pattern-based validation.
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 Cloud DLP to classify and validate emails.
Why it's wrong here
Cloud DLP classifies and de-identifies sensitive data; it does not define row-level validation rules evaluated by Dataplex data quality scans. It is tempting because DLP recognises email infoTypes, but that is for discovery and masking, whereas the requirement is a declarative quality rule with pass/fail results.
- ✗
Use the built-in 'email' rule type in Dataplex.
Why it's wrong here
Dataplex data quality rules support regex and SQL expression rule types, not a built-in 'email' rule type, so this rule cannot be authored. It is tempting because a ready-made email validator would be convenient, but such predefined rule types do not exist in the Dataplex specification.
- ✓
Create a custom Data Quality rule using the 'regex' type.
Why this is correct
Dataplex data quality rules support a regex rule type, letting you define a pattern that each value in the email column must match. This satisfies the requirement to validate email format without writing custom SQL assertions.
- ✗
Create a Dataflow pipeline to validate emails and write results to a separate table.
Why it's wrong here
While a Dataflow pipeline could validate email formats, it operates as an external processing job rather than a native Dataplex rule, so it cannot be defined and managed within Dataplex’s own data quality engine. This option is tempting because Dataflow is commonly used for custom data validation at scale, and it would be correct if the requirement were to perform complex, stateful transformations or integrate with external APIs, rather than applying a simple, declarative quality rule directly inside Dataplex.
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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 Google Cloud exam blueprint
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