Quick Answer
The correct answer is UNNEST with CROSS JOIN because this construct is the standard and most efficient method in BigQuery to flatten nested arrays, also known as repeated fields, into a flat, relational table. When JSON logs contain arrays of structs, CROSS JOIN UNNEST(array_column) expands each element of the array into its own row, allowing BI tools to directly access individual fields without complex subqueries. On the Google Professional Cloud Database Engineer exam, this concept frequently appears in scenarios involving streaming ingestion of semi-structured data, testing your ability to transform nested schemas for analytical queries. A common trap is attempting to use LEFT JOIN with UNNEST, which preserves rows with empty arrays but can confuse the intended flattening logic—CROSS JOIN UNNEST is the idiomatic choice for guaranteed expansion. Memory tip: think of UNNEST as “unpacking a suitcase” and CROSS JOIN as “laying each item on its own table row.”
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. 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 data engineering team ingests JSON logs into BigQuery using a streaming pipeline. Queries need to extract specific fields from nested arrays. Which SQL construct should be used to efficiently transform the nested data into a flat table for BI?
ARRAY_AGG with STRUCT
Why wrong: ARRAY_AGG aggregates rows into arrays, which is the opposite of flattening.
STRUCT with nested field access
Why wrong: STRUCT is used to create groups, not to flatten arrays.
SELECT * EXCEPT with UNNEST
Why wrong: SELECT * EXCEPT removes columns but does not flatten arrays.
UNNEST with CROSS JOIN
UNNEST flattens arrays into rows, allowing access to nested fields.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
UNNEST with CROSS JOIN
Option D is correct because `UNNEST` with `CROSS JOIN` is the standard SQL construct in BigQuery to flatten nested arrays (repeated fields) into a flat table. When JSON logs contain arrays of structs, `CROSS JOIN UNNEST(array_column)` expands each array element into its own row, allowing BI tools to access individual fields directly. This is the most efficient and idiomatic way to transform nested data into a relational format for querying.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
ARRAY_AGG with STRUCT
Why it's wrong here
ARRAY_AGG aggregates rows into arrays, which is the opposite of flattening.
STRUCT with nested field access
Why it's wrong here
STRUCT is used to create groups, not to flatten arrays.
SELECT * EXCEPT with UNNEST
Why it's wrong here
SELECT * EXCEPT removes columns but does not flatten arrays.
UNNEST with CROSS JOIN
Why this is correct
UNNEST flattens arrays into rows, allowing access to nested fields.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the confusion between aggregation (`ARRAY_AGG`) and unnesting (`UNNEST`), where candidates mistakenly think `ARRAY_AGG` can flatten data because it deals with arrays, but it actually does the reverse operation.
Detailed technical explanation
Under the hood, `CROSS JOIN UNNEST` in BigQuery performs a lateral cross join that expands each array element into a separate row, preserving the original row's other columns. This is essential for handling JSON logs with repeated fields, such as user events or product items, where each element must be queried individually. A real-world scenario is analyzing clickstream data where each session contains an array of pageviews—flattening with `UNNEST` allows BI tools to compute metrics per pageview without complex scripting.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — Read the scenario before looking for a memorised answer..
The correct answer is: UNNEST with CROSS JOIN — Option D is correct because `UNNEST` with `CROSS JOIN` is the standard SQL construct in BigQuery to flatten nested arrays (repeated fields) into a flat table. When JSON logs contain arrays of structs, `CROSS JOIN UNNEST(array_column)` expands each array element into its own row, allowing BI tools to access individual fields directly. This is the most efficient and idiomatic way to transform nested data into a relational format for querying.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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