- A
dbt hooks with BigQuery STRUCT access
Why wrong: STRUCT access retrieves fields from structs but does not normalize arrays into rows.
- B
dbt models with BigQuery UNNEST and CROSS JOIN
UNNEST flattens arrays into rows, and CROSS JOIN with UNNEST is the standard way to normalize nested data in BigQuery.
- C
dbt snapshots with BigQuery JSON functions
Why wrong: JSON functions are for JSON data, not for native arrays and structs.
- D
dbt seeds with BigQuery ARRAY_AGG
Why wrong: ARRAY_AGG does the opposite: it aggregates rows into arrays.
Question 67 of 1,000
PDE Ingesting and Processing the Data Practice Question
This PDE practice question tests your understanding of ingesting and processing the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A team uses dbt on BigQuery to transform data in their data warehouse. They have a large table with nested and repeated fields (arrays and structs). The transformation needs to normalize this data into a star schema. Which dbt feature and BigQuery SQL feature should they use together?
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
✓
dbt models with BigQuery UNNEST and CROSS JOIN
To normalize nested and repeated fields (arrays and structs) into a star schema, you need to flatten the arrays into separate rows. BigQuery's UNNEST operator, when used with CROSS JOIN, expands each array element into its own row, effectively denormalizing the nested structure. dbt models (SQL SELECT statements) are the correct dbt feature to define these transformations as version-controlled, reusable SQL files. Together, they allow you to write a dbt model that uses CROSS JOIN UNNEST to produce dimension and fact tables from a single nested table.
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.
- ✗
dbt hooks with BigQuery STRUCT access
Why it's wrong here
STRUCT access retrieves fields from structs but does not normalize arrays into rows.
- ✓
dbt models with BigQuery UNNEST and CROSS JOIN
Why this is correct
UNNEST flattens arrays into rows, and CROSS JOIN with UNNEST is the standard way to normalize nested data in BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
dbt snapshots with BigQuery JSON functions
Why it's wrong here
JSON functions are for JSON data, not for native arrays and structs.
- ✗
dbt seeds with BigQuery ARRAY_AGG
Why it's wrong here
ARRAY_AGG does the opposite: it aggregates rows into arrays.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between features that manipulate data structure (UNNEST) versus features for data lifecycle (snapshots, hooks) or data loading (seeds), leading candidates to confuse the purpose of dbt hooks or snapshots with transformation logic.
Detailed technical explanation
How to think about this question
Under the hood, BigQuery stores nested and repeated fields as a columnar format with child columns; UNNEST converts an ARRAY into a set of rows, and CROSS JOIN (or implicit comma cross join) pairs each row of the parent table with each element of the array, producing a Cartesian product. A subtle behavior is that if the array is empty, CROSS JOIN UNNEST will eliminate that row entirely unless you use LEFT JOIN UNNEST to preserve it. In a real-world star schema normalization, you would typically create separate dbt models for each dimension (e.g., customers, products) using DISTINCT on unnested fields, and a fact table that joins back to the original row-level data.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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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FAQ
Questions learners often ask
What does this PDE question test?
Ingesting and Processing the Data — This question tests Ingesting and Processing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: dbt models with BigQuery UNNEST and CROSS JOIN — To normalize nested and repeated fields (arrays and structs) into a star schema, you need to flatten the arrays into separate rows. BigQuery's UNNEST operator, when used with CROSS JOIN, expands each array element into its own row, effectively denormalizing the nested structure. dbt models (SQL SELECT statements) are the correct dbt feature to define these transformations as version-controlled, reusable SQL files. Together, they allow you to write a dbt model that uses CROSS JOIN UNNEST to produce dimension and fact tables from a single nested table.
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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