The answer is to rewrite the function as a SQL UDF to avoid JavaScript overhead. This is the correct fix because JavaScript UDFs in BigQuery run in a sandbox with a strict memory limit—typically 6 MB per UDF instance—and when processing a large table, per-row overhead or large intermediate results can quickly exhaust this allocation, triggering the “Resources exceeded: UDF out of memory” error. SQL UDFs, by contrast, execute natively within BigQuery’s distributed execution engine, which has no such per-instance memory cap and can handle massive datasets efficiently. On the Google Professional Cloud Database Engineer exam, this question tests your understanding of BigQuery’s execution model and the trade-offs between UDF types; a common trap is to try increasing memory or using a temporary table, but the real constraint is the JavaScript sandbox itself. Remember the mnemonic: “JS is cramped, SQL is ramped”—when memory errors strike, switch to SQL UDFs to let BigQuery’s native engine handle the load.
PCDE Practice Question: Define data structures and implement SQL for Business Intelligence
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. 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.
Exhibit
Refer to the exhibit.
-- BigQuery SQL:
CREATE TEMP FUNCTION normalize_json(json_str STRING)
RETURNS STRING
LANGUAGE js AS """
// Complex JavaScript transformation
var obj = JSON.parse(json_str);
// many operations...
return JSON.stringify(obj);
""";
SELECT
id,
normalize_json(raw_json) AS normalized
FROM
`project.dataset.input`;
The query above fails with 'Resources exceeded: UDF out of memory' on a large table. What is the best way to fix this?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Refer to the exhibit.
-- BigQuery SQL:
CREATE TEMP FUNCTION normalize_json(json_str STRING)
RETURNS STRING
LANGUAGE js AS """
// Complex JavaScript transformation
var obj = JSON.parse(json_str);
// many operations...
return JSON.stringify(obj);
""";
SELECT
id,
normalize_json(raw_json) AS normalized
FROM
`project.dataset.input`;
A
Rewrite the function as a SQL UDF to avoid JavaScript overhead
SQL UDFs run natively in BigQuery's execution engine and do not have the same memory constraints.
B
Add a GROUP BY clause to reduce the number of rows processed
Why wrong: The UDF is applied row by row; grouping does not reduce per-row memory usage.
C
Convert the temporary UDF to a persistent UDF
Why wrong: Persistent UDFs also run in JavaScript and have the same memory limits.
D
Increase the memory allocation for JavaScript UDFs
Why wrong: BigQuery does not allow users to configure UDF memory limits.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Rewrite the function as a SQL UDF to avoid JavaScript overhead
Option A is correct because JavaScript UDFs in BigQuery run in a sandbox with limited memory (typically 6 MB per UDF instance). When processing a large table, the UDF may exceed this memory due to per-row overhead or large intermediate results. Rewriting the function as a SQL UDF eliminates JavaScript overhead and runs natively within BigQuery's distributed execution engine, which can handle larger datasets without memory constraints.
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.
✓
Rewrite the function as a SQL UDF to avoid JavaScript overhead
Why this is correct
SQL UDFs run natively in BigQuery's execution engine and do not have the same memory constraints.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add a GROUP BY clause to reduce the number of rows processed
Why it's wrong here
The UDF is applied row by row; grouping does not reduce per-row memory usage.
✗
Convert the temporary UDF to a persistent UDF
Why it's wrong here
Persistent UDFs also run in JavaScript and have the same memory limits.
✗
Increase the memory allocation for JavaScript UDFs
Why it's wrong here
BigQuery does not allow users to configure UDF memory limits.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that memory errors in UDFs can be fixed by increasing resources or changing UDF persistence, when the real limitation is the fixed JavaScript sandbox memory that can only be avoided by using SQL UDFs.
Detailed technical explanation
How to think about this question
JavaScript UDFs in BigQuery execute in a V8 sandbox with a hard memory limit of 6 MB per UDF invocation, and each row processed by the UDF consumes memory for the input row, output row, and any intermediate objects. SQL UDFs, by contrast, are compiled into BigQuery's native execution plan, allowing them to leverage the full memory and parallelism of the underlying Dremel engine. A real-world scenario is when a JavaScript UDF performs complex string manipulation or array construction on a table with millions of rows, causing out-of-memory errors that are resolved by converting to SQL UDFs using functions like REGEXP_EXTRACT or ARRAY_AGG.
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.
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..
What is the correct answer to this question?
The correct answer is: Rewrite the function as a SQL UDF to avoid JavaScript overhead — Option A is correct because JavaScript UDFs in BigQuery run in a sandbox with limited memory (typically 6 MB per UDF instance). When processing a large table, the UDF may exceed this memory due to per-row overhead or large intermediate results. Rewriting the function as a SQL UDF eliminates JavaScript overhead and runs natively within BigQuery's distributed execution engine, which can handle larger datasets without memory constraints.
What should I do if I get this PCDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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