Snowflake Time Travel is a powerful feature that allows users to access historical data within a defined period. It enables two key capabilities:
B). Restoring data-related objects that have been deleted within the past 90 days: Time Travel can be used to restore tables, schemas, and databases that have been accidentally or intentionally deleted within the Time Travel retention period.
C). Conducting point-in-time analysis for BI reporting: It allows users to query historical data as it appeared at a specific point in time within the Time Travel retention period, which is crucial for business intelligence and reporting purposes.
While Time Travel does allow querying of past data, it is limited to the retention period set for the Snowflake account, which is typically 1 day for standard accounts and can be extended up to 90 days for enterprise accounts. It does not enable querying or restoring objects created or deleted beyond the retention period, nor does it provide analysis over all periods of time.
References:
Snowflake Documentation on Time Travel
SnowPro Core Certification Study Guide

The correct answer is C. Queries will continue running, using the current warehouse size .
When a virtual warehouse is resized while it is running, the resize does not affect queries that are already in progress. Running queries continue using the warehouse resources that were available when they started. The new warehouse size applies to queries that start after the resize operation.
Why C is correct:
Snowflake does not cancel or restart existing queries when a warehouse is resized. In-progress queries continue executing with the current warehouse size.
Why the other options are incorrect:
A). Resizing a warehouse does not automatically cancel in-progress queries.
B). In-progress queries do not switch to the new warehouse size.
D). Queries are not paused and reprocessed after resizing.
Official Snowflake documentation reference:
Snowflake documentation explains that resizing a running warehouse affects only new queries and queued queries. Currently running queries continue to use the warehouse size that was in effect when they started.
Reference: Snowflake Documentation - Resizing a warehouse; Snowflake Documentation - Virtual warehouses; SnowPro Core Study Guide - Snowflake Account and Resource Management.
==

The correct answer is B. To track DML changes that have been made to tables .
A stream is a Snowflake object used for change data capture. It records changes made to a source table so those changes can be consumed later by SQL logic, usually as part of an incremental processing pipeline.
Why B is correct:
Streams track DML changes such as:
INSERT
UPDATE
DELETE
They also expose metadata columns such as:
METADATA$ACTION
METADATA$ISUPDATE
METADATA$ROW_ID
These columns help identify the type of change and the affected row.
Why the other options are incorrect:
A). Tasks execute scheduled SQL and can be used to run data pipelines. Streams track the changes consumed by those pipelines.
C). File formats define how staged files are interpreted during loading or unloading. Streams are unrelated to file format definitions.
D). Caching is handled by Snowflake result cache and warehouse cache mechanisms, not by streams.
Official Snowflake documentation reference:
Snowflake documentation describes streams as objects that record DML changes made to tables, including inserts, updates, and deletes.
Reference: Snowflake Documentation - Streams; Snowflake Documentation - Change data capture; SnowPro Core Study Guide - SQL and Snowflake Objects.
==

The Snowflake data types that can be used to build nested hierarchical data are OBJECT and VARIANT. These data types support the storage and querying of semi-structured data, allowing for the creation of complex, nested data structures