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  2. Snowflake Certification
  3. SnowPro-Core Exam
  4. Snowflake.SnowPro-Core.v2024-10-07.q429 Dumps
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Question 341

What is one of the characteristics of data shares?

Correct Answer: C
Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.
References:
* Snowflake Documentation: Understanding Secure Views in Data Sharing
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Question 342

What are the three layers that make up Snowflake's architecture?
Choose 3 answer

Correct Answer: A,C,D
Reference: https://docs.snowflake.com/en/user-guide/intro-key-concepts.html
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Question 343

Snowflake recommends, as a minimize, that all users with the following roles(s) should be enrolled in Multi-Factor Authentication (MFA):
Select one:

Correct Answer: B
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Question 344

Fail-safe is unavailable on which table types?

Correct Answer: A,B
Reference: https://docs.snowflake.com/en/user-guide/tables-temp-transient.html
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Question 345

What are potential impacts of storing non-native values like dates and timestamps in a variant column in Snowflake?

Correct Answer: B
Storing non-native values, such as dates and timestamps, in a VARIANT column in Snowflake can lead to slower query performance and increased storage consumption. VARIANT is a semi-structured data type that allows storing JSON, AVRO, ORC, Parquet, or XML data in a single column. When non-native data types are stored as VARIANT, Snowflake must perform implicit conversion to process these values, which can slow down query execution. Additionally, because the VARIANT data type is designed to accommodate a wide variety of data formats, it often requires more storage space compared to storing data in native, strongly-typed columns that are optimized for specific data types.
The performance impact arises from the need to parse and interpret the semi-structured data on the fly during query execution, as opposed to directly accessing and operating on optimally stored data in its native format.
Furthermore, the increased storage consumption is a result of the overhead associated with storing data in a format that is less space-efficient than the native formats optimized for specific types of data.
References:
* Snowflake Documentation on Semi-Structured Data: Semi-Structured Data
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