The correct answer is B. PARTITION BY .
The PARTITION BY option in COPY INTO < location > partitions unloaded data into separate files based on the expression specified. This is commonly used to organize unloaded files by values such as date, region, or another column.
Why B is correct:
PARTITION BY controls how unloaded rows are grouped into different output paths/files.
Example:
COPY INTO @my_stage/sales/
FROM sales
PARTITION BY (TO_DATE(sale_date))
FILE_FORMAT = (TYPE = CSV);
This unloads rows into separate output paths based on the sale_date expression.
Why the other options are incorrect:
A). DETAILED_OUTPUT controls the amount of unload result information returned by the command.
C). FILE_FORMAT controls the format of the unloaded files, such as CSV, JSON, or Parquet.
D). HEADER controls whether column headings are included in unloaded output files.
Official Snowflake documentation reference:
Snowflake documentation for COPY INTO < location > describes PARTITION BY as the option used to partition unloaded rows into separate files based on an expression.
Reference: Snowflake Documentation - COPY INTO < location > ; Snowflake Documentation - Unloading data with partitioning; SnowPro Core Study Guide - Data Loading and Unloading.

Snowflake utilizes clustering information to enhance query performance by pruning unnecessary micro-partitions.
Clustering Metadata: Snowflake stores clustering information for each micro-partition, which includes data range and distribution.
Pruning Micro-partitions: When a query is executed, Snowflake uses this clustering metadata to identify and eliminate micro-partitions that do not match the query criteria, thereby reducing the amount of data scanned and improving query performance.
Reference:
Snowflake Documentation on Clustering
Snowflake Documentation on Micro-partition Pruning

Explanation
https://docs.snowflake.com/en/user-guide/data-load-considerations-prepare.html

When SINGLE = FALSE is set during COPY INTO, Snowflake writes multiple output files, enabling parallelism. Setting SINGLE = TRUE forces one output file, reducing parallel processing.
Reference:
Snowflake Docs: COPY INTO Parameters

The Merge operator is considered a DML (Data Manipulation Language) operator in Snowflake. DML operators are used to modify data in tables, and the Merge operator specifically allows for conditional updates, inserts, or deletes in a target table based on the results of a join with a source table.
Snowflake Documentation: Query Profile Overview
Snowflake Documentation: MERGE Statement