Which Snowflake tasks will take advantage of underlying micro-partition metadata? Select TWO.
Correct Answer: A,E
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. ==
Question 447
Which table type exists only within the session it was created and is only visible to the user who created it?
Correct Answer: D
The correct answer is D. Temporary . A temporary table exists only for the duration of the session in which it was created. It is only visible to the user session that created it and is automatically dropped when the session ends. Why D is correct: Temporary tables are useful for session-specific intermediate data. They do not persist beyond the session and are not visible to other users or sessions. Example: CREATE TEMPORARY TABLE temp_sales AS SELECT * FROM sales WHERE sales_date = CURRENT_DATE; Why the other options are incorrect: A). Hybrid tables are optimized for transactional workloads and are not session-only objects. B). Dynamic tables are used for declarative, automated data transformation and are persistent database objects. C). Transient tables persist until explicitly dropped and are visible according to granted privileges. They do not have Fail-safe, but they are not session-only. Official Snowflake documentation reference: Snowflake documentation describes temporary tables as existing only within the session in which they are created and being dropped automatically at the end of the session. Reference: Snowflake Documentation - Temporary tables; Snowflake Documentation - Table types; SnowPro Core Study Guide - SQL and Snowflake Objects.
Question 448
A JSON document is stored in the source_colum of type VARIANT. The document has an array called elements. The array contains the name key that has a string value How can a Snowflake user extract the name from the first element?
Correct Answer: C
In Snowflake, when dealing with semi-structured data such as a JSON document stored in aVARIANTcolumn, the proper syntax to extract a value is to use the column name followed by the path to the specific element. Since arrays in JSON are zero-indexed, the first element is referenced with[0]. Therefore, to extract thenamefrom the first element of theelementsarray, the correct syntax isSource_column:elements[0] . name. References: Snowflake Documentation on Semi-Structured Data
Question 449
True or False: A 4X-Large Warehouse may, at times, take longer to provision than a X-Small Warehouse.
Correct Answer: A
Provisioning time can vary based on the size of the warehouse. A 4X-Large Warehouse typically has more resources and may take longer to provision compared to a X-Small Warehouse, which has fewer resources and can generally be provisioned more quickly.References:Understanding and viewing Fail-safe | Snowflake Documentation
Question 450
How long is Snowpipe data load history retained?
Correct Answer: C
Snowpipe data load history is retained for 64 days. This retention period allows users to review and audit the data load operations performed by Snowpipe over a significant period of time, which can be crucial for troubleshooting and ensuring data integrity. References: [COF-C02] SnowPro Core Certification Exam Study Guide Snowflake Documentation on Snowpipe1
