The correct answers are A. Query pruning and E. Operations using Data Manipulation Language, or DML .
Snowflake automatically gathers and maintains metadata about micro-partitions. This metadata includes information such as the range of values in columns, distinct value counts, and other statistics. Snowflake uses this metadata to optimize query execution and table operations.
Why A is correct:
Query pruning is one of the most important uses of micro-partition metadata. Snowflake can skip micro- partitions that do not contain values needed by a query predicate.
Example:
SELECT *
FROM sales
WHERE sale_date = ' 2026-01-01 ' ;
If Snowflake knows from micro-partition metadata that certain micro-partitions do not contain sale_date = '
2026-01-01 ' , those micro-partitions can be skipped.
Why E is correct:
DML operations such as UPDATE, DELETE, and MERGE can also benefit from micro-partition metadata.
Snowflake can use metadata to identify which micro-partitions are affected by the operation instead of scanning unnecessary partitions.
Why the other options are incorrect:
B). The result cache returns previously computed query results. It avoids re-executing the query and therefore does not rely on micro-partition pruning in the same way.
C). Local disk cache stores data on warehouse compute resources after access, but it is not the main feature that uses micro-partition metadata.
D). "Remote disk cache" is not the standard Snowflake concept tested here. Snowflake has remote storage, warehouse cache, and result cache, but query pruning is the metadata-driven feature.
Official Snowflake documentation reference:
Snowflake documentation explains that micro-partition metadata is used for efficient query pruning and optimization. It also supports efficient table maintenance and DML operations by helping Snowflake identify relevant micro-partitions.
Reference: Snowflake Documentation - Micro-partitions and data clustering; Snowflake Documentation - Query pruning; SnowPro Core Study Guide - Snowflake Architecture.

To process unstructured data in Snowflake, external functions can be used.
External Functions: These allow you to call external services and processing engines from within Snowflake SQL. External functions can be used to handle complex processing tasks that are not natively supported by Snowflake, including those involving unstructured data.
Implementation: You define an external function in Snowflake that points to an external processing service (e.g., AWS Lambda, Google Cloud Functions).
Reference:
Snowflake Documentation on External Functions

The Search Optimization Service in Snowflake is designed to improve the performance of specific types of queries, particularly those involving extensive sets of WHERE conditions. By maintaining a search index on tables, this service can accelerate lookup and analytical queries, making it a valuable feature for optimizing query performance and reducing execution times for complex searches.
Reference:
Snowflake Documentation: Search Optimization Service

To insert JSON formatted string data into a VARIANT field in Snowflake, the correct function to use is PARSE_JSON. The PARSE_JSON function is specifically designed to interpret a JSON formatted string and convert it into a VARIANT type, which is Snowflake's flexible format for handling semi-structured data like JSON, XML, and Avro. This function is essential for loading and querying JSON data within Snowflake, allowing users to store and manage JSON data efficiently while preserving its structure for querying purposes.
This function's usage and capabilities are detailed in the Snowflake documentation, providing users with guidance on how to handle semi-structured data effectively within their Snowflake environments.
References:
* Snowflake Documentation: PARSE_JSON