A Snowflake table 'ORDERS' contains a 'ORDER DATE' column of data type DATE. You need to write a query that returns the order count for each month in the year 2023. Which of the following SQL snippets

Question

Exhibit

Options

• AOption A
• BOption B
• COption C
• DOption D
• EOption E

• A
  18% (8)
• B
  9% (4)
• C
  64% (28)
• D
  7% (3)
• E
  2% (1)

Explanation

The actual SQL code for options A–E wasn't included in your message - only the labels are shown. Without the snippets, I can't explain why each specific distractor is wrong.

That said, here's what makes an answer correct for this type of Snowflake question, which you can map to option C yourself:

The correct approach groups by truncated month using DATE_TRUNC('MONTH', ORDER_DATE) and filters to 2023 with a WHERE clause, like:

SELECT DATE_TRUNC('MONTH', ORDER_DATE) AS order_month, COUNT(*) AS order_count
FROM ORDERS
WHERE YEAR(ORDER_DATE) = 2023
GROUP BY 1
ORDER BY 1;

Common distractor patterns and why they fail:

• Using TO_CHAR(ORDER_DATE, 'YYYY-MM') - works but is less efficient; it converts to string, preventing partition pruning
• Grouping by MONTH(ORDER_DATE) alone - collapses months across years (January 2022 and January 2023 merge)
• Using DATEPART - SQL Server syntax, not valid Snowflake
• Filtering with ORDER_DATE LIKE '2023%' - invalid on a DATE type column

Memory tip: In Snowflake, always prefer DATE_TRUNC over string-based date formatting when grouping by time periods - it keeps the column as a native DATE/TIMESTAMP, enabling micro-partition pruning and accurate sorting.

If you paste the actual A–E SQL snippets, I can give a precise per-option breakdown.

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