Snowflake SnowPro Core Practice Test - Questions Answers, Page 6

In all Snowflake editions, two key capabilities are universally available:

B) Automatic encryption of all data: Snowflake automatically encrypts all data stored in its platform, ensuring security and compliance with various regulations. This encryption is transparent to users and does not require any configuration or management.

D) Object-level access control: Snowflake provides granular access control mechanisms that allow administrators to define permissions at the object level, including databases, schemas, tables, and views. This ensures that only authorized users can access specific data objects.

These features are part of Snowflake's commitment to security and governance, and they are included in every edition of the Snowflake Data Cloud.

Snowflake Documentation on Security Features

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TheCOPY INTOcommand is used in Snowflake to unload data from a table into a file in a stage. This command allows for the export of data from Snowflake tables into flat files, which can then be used for further analysis, processing, or storage in external systems.

Snowflake Documentation on Unloading Data

Snowflake SnowPro Core: Copy Into Command to Unload Rows to Files in Named Stage

Snowflake automatically rotates encryption keys when they are more than 30 days old. Active keys are retired, and new keys are created. This process is part of Snowflake's comprehensive security measures to ensure data protection and is managed entirely by the Snowflake service without requiring user intervention.

Understanding Encryption Key Management in Snowflake

A VARIANT column in Snowflake can store semi-structured data types. This includes:

B) OBJECT: An object is a collection of key-value pairs in JSON, and a VARIANT column can store this type of data structure.

D) ARRAY: An array is an ordered list of zero or more values, which can be of any variant-supported data type, including objects or other arrays.

The VARIANT data type is specifically designed to handle semi-structured data like JSON, Avro, ORC, Parquet, or XML, allowing for the storage of nested and complex data structures.

Snowflake Documentation on Semi-Structured Data Types

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The most efficient way to get files into Snowflake, especially when new files are being written to a cloud storage location at frequent intervals, is to use Snowpipe with auto-ingest. Snowpipe is Snowflake's continuous data ingestion service that loads data as soon as it becomes available in a cloud storage location. By setting up cloud provider notifications, Snowpipe can be triggered automatically whenever new files are written to the storage location, ensuring that the data is loaded into Snowflake with minimal latency and without the need for manual intervention or scheduling frequent tasks.

Snowflake Documentation on Snowpipe

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When loading data into Snowflake, it is recommended to:

C) Load files that are approximately 100-250 MB (or larger): This size is optimal for parallel processing and can help to maximize throughput. Smaller files can lead to overhead that outweighs the actual data processing time.

D) Avoid using embedded characters such as commas for numeric data types: Embedded characters can cause issues during data loading as they may be interpreted incorrectly. It's best to clean the data of such characters to ensure accurate and efficient data loading.

These best practices are designed to optimize the data loading process, ensuring that data is loaded quickly and accurately into Snowflake.

Snowflake Documentation on Data Loading Considerations

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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.

Snowflake Documentation on Data Loading

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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.

Snowflake Documentation on Stages

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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.

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

Snowflake SnowPro Core Certification Exam Questions Set 10