- A
Row-level rules (e.g., condition must be true for every row)
Row-level rules validate each row against a condition.
- B
Set-level rules (e.g., intersection or difference between two tables)
Why wrong: Set-level rules are not supported in Dataplex's SQL-based rules.
- C
Pattern matching rules (e.g., regex on column values)
Why wrong: While regex can be used in SQL, pattern matching is not a distinct rule type; it's covered by column-level rules.
- D
Table-level rules (e.g., row count threshold)
Table-level rules check aggregate properties like row count.
- E
Column-level rules (e.g., uniqueness, nullness, range)
Column-level rules apply to specific columns.
Question 514 of 1,000
PDE Maintaining and Automating Data Workloads Practice Question
This PDE practice question tests your understanding of maintaining and automating data workloads. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are configuring Dataplex data quality rules for a BigQuery table. Which three types of rules can be defined using Dataplex's SQL-based rule engine? (Choose three.)
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
✓
Row-level rules (e.g., condition must be true for every row)
Option A is correct because Dataplex's SQL-based rule engine supports row-level rules that enforce a condition that must be true for every row in a BigQuery table. These rules are defined using a SQL expression that is evaluated per row, and if any row fails the condition, the rule is violated. This allows you to validate data integrity at the most granular level, such as ensuring a column value is always positive.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Row-level rules (e.g., condition must be true for every row)
Why this is correct
Row-level rules validate each row against a condition.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set-level rules (e.g., intersection or difference between two tables)
Why it's wrong here
Set-level rules are not supported in Dataplex's SQL-based rules.
- ✗
Pattern matching rules (e.g., regex on column values)
Why it's wrong here
While regex can be used in SQL, pattern matching is not a distinct rule type; it's covered by column-level rules.
- ✓
Table-level rules (e.g., row count threshold)
Why this is correct
Table-level rules check aggregate properties like row count.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Column-level rules (e.g., uniqueness, nullness, range)
Why this is correct
Column-level rules apply to specific columns.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between rule categories that are natively supported versus those that require custom SQL workarounds, leading candidates to mistakenly select set-level or pattern matching rules as separate types when they are actually implemented within row-level rules.
Detailed technical explanation
How to think about this question
Under the hood, Dataplex translates each SQL-based rule into a BigQuery query that scans the target table; for row-level rules, it uses a WHERE clause to identify failing rows, while table-level rules use aggregate functions like COUNT(*) to check thresholds. A subtle behavior is that row-level rules can be combined with column-level rules in a single rule set, but each rule type is evaluated independently, and the engine logs all violations to the Dataplex Data Quality dashboard. In a real-world scenario, you might use a row-level rule to ensure that a 'transaction_date' column is not in the future, while a table-level rule verifies that the total row count exceeds a minimum threshold after a daily load.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Maintaining and Automating Data Workloads — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Maintaining and Automating Data Workloads — This question tests Maintaining and Automating Data Workloads — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Row-level rules (e.g., condition must be true for every row) — Option A is correct because Dataplex's SQL-based rule engine supports row-level rules that enforce a condition that must be true for every row in a BigQuery table. These rules are defined using a SQL expression that is evaluated per row, and if any row fails the condition, the rule is violated. This allows you to validate data integrity at the most granular level, such as ensuring a column value is always positive.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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