PDE Maintaining and Automating Data Workloads Practice Question
A team wants to enforce data quality rules on BigQuery tables using Dataplex. They need to run column-level checks for null values and row-level checks for value ranges on a schedule. Which Dataplex feature should they use?
⚠ Common exam trap
PDE often tests the distinction between Dataplex Data Quality and Data Profiling, so candidates might choose Data Profiling for rule enforcement when it's actually for analysis.
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
✓
Dataplex Data Quality Tasks
Dataplex Data Quality Tasks allow you to define and run data quality rules on BigQuery tables, including column-level checks (e.g., null checks) and row-level checks (e.g., value ranges). These tasks can be scheduled to run periodically, and they generate results that can be monitored. This is the native Dataplex feature for enforcing data quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dataplex Data Profiling
Why it's wrong here
Data Profiling generates statistical summaries of existing data, such as distinct counts and distributions, rather than enforcing pass/fail rules. Dataplex data quality tasks evaluate null and range conditions and publish results. Profiling is tempting because it examines columns, but it reports observations instead of applying the defined quality rules.
- ✗
BigQuery stored procedures with scheduled queries
Why it's wrong here
Stored procedures with scheduled queries execute SQL but produce no Dataplex data quality scores, rule definitions or integrated reporting. Dataplex data quality tasks natively run column and row checks on a schedule. Hand-rolled SQL is tempting for its flexibility, yet it bypasses the governance metadata the requirement implies.
- ✓
Dataplex Data Quality Tasks
Why this is correct
Dataplex Data Quality Tasks run scheduled, rule-based checks directly against BigQuery tables, supporting both column-level null validation and row-level range conditions. This satisfies the stem's requirement for automated, recurring enforcement of data quality rules without external tooling, unlike profiling or discovery features that only observe metadata.
- ✗
Cloud DLP inspection jobs
Why it's wrong here
Cloud DLP inspection jobs discover and classify sensitive data such as personally identifiable information; they do not evaluate null counts or value-range rules. Dataplex data quality tasks define exactly those column and row checks. DLP is tempting because it also scans BigQuery tables, but its purpose is classification, not rule-based validation.
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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 Google Cloud exam blueprint
This PDE practice question is part of Courseiva's free Google Cloud 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 PDE exam.