Snowflake COF-C02 Practice Test - Questions Answers, Page 6

When theCOPY INTOcommand is executed in Snowflake, it processes all files present in the specified stage that have not been ingested before or marked as already loaded. Since the user did not remove the original 10 files after the first load, running the sameCOPY INTOcommand again will result in all 20 files being processed. This means that the data from the original 10 files will be appended to the table again, along with the data from the new 10 files, potentially leading to duplicate records for the original data set.

References:

Snowflake Documentation on Data Loading

SnowPro Core Certification Study Guide

Thelistcommand in Snowflake is used to validate and display the list of files in a specified stage. When a user has unloaded data to a stage, running thelist @file__stagecommand will show all the files that have been uploaded to that stage, allowing the user to verify the data that was unloaded.

References:

Snowflake Documentation on Stages

SnowPro Core Certification Study Guide

When a cloned table is replicated to a secondary database in Snowflake, the following occurs:

C . The physical data is replicated: The actual data of the cloned table is physically replicated to the secondary database.This ensures that the secondary database has its own copy of the data, which can be used for read-only purposes or failover scenarios1.

E . Metadata pointers to cloned tables are replicated: Along with the physical data, the metadata pointers that refer to the cloned tables are also replicated.This metadata includes information about the structure of the table and any associated properties2.

It's important to note that while the physical data and metadata are replicated, the secondary database is typically read-only and cannot be used for write operations. Additionally, while there may be additional storage costs associated with the secondary account, this is not a direct result of the replication process but rather a consequence of storing additional data.

References:

SnowPro Core Exam Prep --- Answers to Snowflake's LEVEL UP: Backup and Recovery

Snowflake SnowPro Core Certification Exam Questions Set 10

Snowflake supports querying semi-structured data using specific data types that are capable of handling the flexibility and structure of such data. The data types supported for this purpose are:

A . VARIANT: This is a universal data type that can store values of any other type, including structured and semi-structured types.It is particularly useful for handling JSON, Avro, ORC, Parquet, and XML data formats1.

B . ARRAY: An array is a list of elements that can be of any data type, including VARIANT, and is used to handle semi-structured data that is naturally represented as a list1.

These data types are part of Snowflake's built-in support for semi-structured data, allowing for the storage, querying, and analysis of data that does not fit into the traditional row-column format.

References:

Snowflake Documentation on Semi-Structured Data

[COF-C02] SnowPro Core Certification Exam Study Guide

Secure sharing in Snowflake allows users to share specific objects with other Snowflake accounts without physically copying the data, thus not consuming additional storage. Tables and Secure User Defined Functions (UDFs) are among the objects that can be shared using this feature. Materialized views, sequences, and procedures are not shareable objects in Snowflake.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Secure Data Sharing1

When a Snowflake virtual warehouse is resized, the data cached in the warehouse is not lost. This is because the cache is maintained independently of the warehouse size. Resizing a warehouse, whether scaling up or down, does not affect the cached data, ensuring that query performance is not impacted by such changes.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouse Performance1

Alation is known for specializing in data catalog solutions and is a partner of Snowflake. Data catalog solutions are essential for organizations to effectively manage their metadata and make it easily accessible and understandable for users, which aligns with the capabilities provided by Alation.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake's official documentation and partner listings

Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries on large datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Loading1

TheCOPY INTOcommand in Snowflake can be configured to output data into a single file by setting theMAX_FILE_NUMBERoption to 1. This option limits the number of files generated by the command, ensuring that only one file is created regardless of the amount of data being exported.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Unloading