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.
In BigQuery, a BI analyst wants to store financial data with high precision and avoid rounding errors. Which data type should be used for currency columns?
NUMERIC
NUMERIC is a fixed-point decimal type designed for financial precision.
FLOAT64
Why wrong: FLOAT64 is approximate and can cause rounding errors in financial calculations.
INT64
Why wrong: INT64 is integer only and cannot represent cents.
STRING
Why wrong: String requires casting and is inefficient for arithmetic.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
NUMERIC
NUMERIC (also known as DECIMAL) is the correct choice because it stores exact numeric values with up to 38 digits of precision and a user-defined scale, making it ideal for financial data where rounding errors from binary floating-point representation are unacceptable. In BigQuery, NUMERIC uses fixed-point arithmetic, ensuring that calculations like tax or interest accruals remain exact to the specified decimal places.
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.
NUMERIC
Why this is correct
NUMERIC is a fixed-point decimal type designed for financial precision.
Related concept
Read the scenario before looking for a memorised answer.
FLOAT64
Why it's wrong here
FLOAT64 is approximate and can cause rounding errors in financial calculations.
INT64
Why it's wrong here
INT64 is integer only and cannot represent cents.
STRING
Why it's wrong here
String requires casting and is inefficient for arithmetic.
Common exam traps
Google Cloud often tests the misconception that FLOAT64 is acceptable for currency because it 'has enough precision,' but the trap is that binary floating-point types inherently cannot represent many decimal fractions exactly, causing cumulative rounding errors in financial data.
Detailed technical explanation
Under the hood, BigQuery's NUMERIC type is implemented as a fixed-point decimal with a precision of 38 digits and a default scale of 9, meaning it can store values like 12345678901234567890.123456789 exactly. This contrasts with FLOAT64, which follows the IEEE 754 double-precision standard and can introduce errors in repeated calculations, such as summing thousands of transactions. In real-world scenarios, using NUMERIC prevents discrepancies in financial reporting, such as balance sheet totals that differ by fractions of a cent due to floating-point accumulation.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: NUMERIC — NUMERIC (also known as DECIMAL) is the correct choice because it stores exact numeric values with up to 38 digits of precision and a user-defined scale, making it ideal for financial data where rounding errors from binary floating-point representation are unacceptable. In BigQuery, NUMERIC uses fixed-point arithmetic, ensuring that calculations like tax or interest accruals remain exact to the specified decimal places.
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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