Virtual warehouses in Snowflake can be started and stopped at any time, providing flexibility in managing compute resources. They can also be resized at any time, even while running, toaccommodate varying workloads910. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide

The correct answer is B. Directory table .
A directory table is not a regular user-created table like a permanent, transient, or temporary table. It is an implicit object associated with a stage. It stores file-level metadata about staged files in cloud storage.
Directory tables are useful when users need to query information about files stored in a stage, such as file names, sizes, last modified timestamps, and relative paths.
Why B is correct:
A directory table stores a catalog of staged files in cloud storage. It allows users to query metadata about files in a stage using SQL.
Example concept:
SELECT *
FROM DIRECTORY(@my_stage);
This returns metadata about files stored in the stage.
Why the other options are incorrect:
A). Transient table stores user data but does not store a catalog of staged files.
C). Temporary table stores session-specific user data and disappears at the end of the session.
D). Permanent table stores persistent user data and supports Fail-safe, but it is not specifically used to catalog staged files.
Official Snowflake documentation reference:
Snowflake documentation describes directory tables as objects that store a catalog of staged files in cloud storage. Directory tables are associated with stages and can be queried to retrieve file metadata.
Reference: Snowflake Documentation - Directory tables; Snowflake Documentation - Stages; SnowPro Core Study Guide - Data Loading and Unloading.
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The correct answers are A. JSON null values and E. Timestamps within strings .
Important correction:
The provided answer A, B is not the best answer. Snowflake documentation specifically calls out that certain values in semi-structured data may not be extracted into separate columnar storage, which can result in slower queries and increased storage usage. Two commonly tested examples are JSON null values and date or timestamp values stored as strings.
Why A is correct:
When a semi-structured element contains JSON null values, Snowflake might not extract that element into a separate column. This can reduce pruning efficiency and require Snowflake to scan more of the VARIANT structure during query execution.
Why E is correct:
Date and timestamp values inside semi-structured data that are stored as strings may not be optimized in the same way as native typed values. Storing timestamps as strings can increase storage and slow query performance because Snowflake may need to parse or cast the values at query time.
Why the other options are incorrect:
B). Floating-point numbers are supported inside VARIANT; they are not the main documented issue in this context.
C). Arrays are supported in semi-structured data. While very complex nested structures can affect query patterns, arrays alone are not the best answer here.
D). Vectors are a Snowflake data type used for vector operations and are not the standard answer for this semi- structured data optimization question.
Official Snowflake documentation reference:
Snowflake documentation explains that for semi-structured data, elements containing JSON null values may not be extracted into separate columns. It also recommends storing date and timestamp values in native typed columns when possible rather than as strings inside VARIANT, to improve query performance and storage efficiency.
Reference: Snowflake Documentation - Considerations for semi-structured data stored in VARIANT; Snowflake Documentation - Semi-structured data types; SnowPro Core Study Guide - Working with Semi- Structured Data.
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The correct answers are A. Increase the size of the virtual warehouse and B. Edit the queries to process the data in smaller batches .
Memory spilling occurs when a query requires more memory than is available in the virtual warehouse.
Snowflake may spill intermediate data to local or remote storage, which can slow query performance.
Why A is correct:
Increasing the warehouse size provides more compute and memory resources. This can reduce or eliminate spilling for memory-intensive queries.
Why B is correct:
Rewriting queries to process data in smaller batches reduces the amount of intermediate data held in memory at one time. This can reduce spilling and improve performance.
Important note about option C:
Option C is misleading because a virtual warehouse cluster's size is changed by increasing the warehouse size
. Since option A already says this correctly, option C is not the best answer.
Why the other options are incorrect:
C). "Increase the size of the virtual warehouse clusters" is essentially a less precise duplicate of increasing warehouse size. The exam-style answer is A.
D). Suspending and resuming a warehouse may clear cache, but it does not increase available memory and does not solve memory spilling.
E). Clustering can improve pruning and reduce scanned data for some queries, but it is not the direct general solution for queries that are spilling because they are too large for memory.
Official Snowflake documentation reference:
Snowflake Query Profile documentation explains that spilling to local or remote storage can occur when operations do not fit in memory. Snowflake performance guidance recommends scaling up the warehouse or rewriting queries to reduce the amount of data processed.
Reference: Snowflake Documentation - Query Profile; Snowflake Documentation - Warehouse performance; SnowPro Core Study Guide - Performance Optimization.
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