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| Section | Objectives |
|---|---|
| Data Sharing and Protection | - Secure data sharing - Data replication and failover concepts |
| Security and Access Control | - Role-based access control (RBAC) - Authentication and user management |
| Snowflake Architecture and Platform Concepts | - Virtual warehouses and compute model - Snowflake architecture fundamentals - Storage and data organization |
| Data Transformation and Processing | - Streams and tasks for data pipelines - SQL operations in Snowflake |
| Performance and Optimization | - Query optimization basics - Warehouse sizing and scaling |
| Data Loading and Unloading | - External and internal stages - Bulk data loading using stages and COPY INTO |
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NEW QUESTION # 743
A JSON file, that contains lots of dates and arrays, needs to be processed in Snowflake. The user wants to ensure optimal performance while querying the data.
How can this be achieved?
Answer: A
Explanation:
Storing JSON data in a table with a VARIANT data type is optimal for querying because it allows Snowflake to leverage its semi-structured data capabilities.This approach enables efficient storage and querying without the need for flattening the data, which can be performance-intensive1.
NEW QUESTION # 744
Why would the REPEATABLE keyword be used in a SAMPLE clause?
Answer: B
Explanation:
The correct answer is C. To ensure the same sample is returned each time a query is run .
The REPEATABLE keyword is used with a seed value to make sampling deterministic. This means Snowflake can return the same sample when the query is rerun with the same seed and the underlying data has not changed.
Why C is correct:
SELECT *
FROM table_A
SAMPLE BERNOULLI (10) REPEATABLE (123);
This returns a repeatable sample based on the seed value 123.
Why the other options are incorrect:
A). Returning a fixed number of rows uses SAMPLE ( < n > ROWS), not REPEATABLE.
B). REPEATABLE does not remove duplicates.
D). Block sampling is controlled with BLOCK or SYSTEM, not REPEATABLE.
Official Snowflake documentation reference:
Snowflake documentation explains that REPEATABLE or SEED specifies a seed value to make sample results repeatable.
Reference: Snowflake Documentation - SAMPLE / TABLESAMPLE; SnowPro Core Study Guide - SQL and Snowflake Objects.
NEW QUESTION # 745
Which command is used in Snowflake to manually refresh a directory table?
Answer: A
Explanation:
The correct answer is B. ALTER STAGE .
Directory tables are associated with stages. To manually refresh the metadata in a directory table, the stage is refreshed using the ALTER STAGE ... REFRESH command.
Why B is correct:
A directory table stores file-level metadata for files in a stage. Since the directory table is tied to the stage, the manual refresh operation is performed through the stage.
Example:
ALTER STAGE my_stage REFRESH;
This refreshes the directory table metadata for the stage.
Why the other options are incorrect:
A). TRUNCATE STAGE is not the command used to refresh directory table metadata.
C). ALTER TABLE is used to modify table properties, not refresh a directory table associated with a stage.
D). ALTER VIEW is used to modify views, not directory tables.
Official Snowflake documentation reference:
Snowflake documentation explains that directory table metadata can be refreshed manually using ALTER STAGE ... REFRESH.
Reference: Snowflake Documentation - Directory tables; Snowflake Documentation - ALTER STAGE; SnowPro Core Study Guide - Data Loading and Unloading.
==
NEW QUESTION # 746
What happens when an external or an internal stage is dropped? (Select TWO).
Answer: B,C
Explanation:
When an external stage is dropped in Snowflake, the reference to the external storage location is removed, but the actual files within the external storage (like Amazon S3, Google Cloud Storage, or Microsoft Azure) are not deleted. This means that the data remains intact in the external storage location, and only the stage object in Snowflake is removed.
On the other hand, when an internal stage is dropped, any files that were uploaded to the stage are deleted along with the stage itself. These files are not recoverable once the internal stage is dropped, as they are permanently removed from Snowflake's storage.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Stages
NEW QUESTION # 747
A Snowflake user is trying to load a 125 GB file using SnowSQL. The file continues to load for almost an entire day. What will happen at the 2 < hour mark?
Answer: A
Explanation:
When attempting to load large files, such as a 125 GB file, into Snowflake using SnowSQL, the process might encounter limitations related to the maximum execution time for queries or data loading operations. If the loading process exceeds this time limit (typically around 24 hours), it could be aborted without committing any part of the file to the database. This behavior is designed to prevent indefinite resource consumption and to maintain system stability, emphasizing the need for optimizing data load operations, possibly through file segmentation or parallel loading strategies.
References: Snowflake Documentation on Data Loading Considerations
NEW QUESTION # 748
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