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.

In Snowflake, tag inheritance is a feature that allows tags, which are key-value pairs assigned to objects for the purpose of data governance and metadata management, to be inherited within a hierarchy. The hierarchy in which tag inheritance is possible is from Schema to Table to Column. This means that a tag applied to a schema can be inherited by the tables within that schema, and a tag applied to a table can be inherited by the columns within that table.
References: Snowflake Documentation on Tagging and Object Hierarchy

The SQL command Select * from table(validate(t1, job_id => '_last')); is used to return errors from the last executed COPY command into table t1 in the current session. It checks the results of the most recent data load operation and provides details on any errors that occurred during that process1.

* Clustering keys improve query performance by organizing data to optimize retrieval for frequently queried columns.
* For high-cardinality columns like a 15-digit ID, Snowflake recommends bucketing or truncating the column values to reduce cardinality.
* The function TRUNC(ID_NUMBER, -6) truncates the ID number by dropping the last 6 digits, reducing the number of unique values while maintaining relevance for clustering.
Key Steps:
* Use TRUNC to reduce high-cardinality column values for efficient clustering.
* Monitor clustering key performance via clustering depth metrics.
References:
* Snowflake Documentation: Clustering Keys
* Snowflake Best Practices for Clustering

Snowflake allows users to write custom application logic in JavaScript for two types of objects: Stored Procedures and User-Defined Functions (UDFs).
Stored Procedures: Snowflake stored procedures can be written in JavaScript to encapsulate complex business logic and procedural operations.
CREATE OR REPLACE PROCEDURE my_procedure()
RETURNS STRING
LANGUAGE JAVASCRIPT
EXECUTE AS CALLER
AS
$$
// JavaScript logic here
$$;
User-Defined Functions (UDFs): Snowflake UDFs can be written in JavaScript to perform custom calculations or operations on data.
CREATE OR REPLACE FUNCTION my_function(x FLOAT)
RETURNS FLOAT
LANGUAGE JAVASCRIPT
AS
$$
return x * 2;
$$;
Reference:
Snowflake Documentation: Stored Procedures
Snowflake Documentation: User-Defined Functions (UDFs)