TEST SNOWPRO-CORE SAMPLE ONLINE - SNOWPRO-CORE VALID EXAM SYLLABUS

Test SnowPro-Core Sample Online - SnowPro-Core Valid Exam Syllabus

Test SnowPro-Core Sample Online - SnowPro-Core Valid Exam Syllabus

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Tags: Test SnowPro-Core Sample Online, SnowPro-Core Valid Exam Syllabus, SnowPro-Core Reliable Exam Materials, SnowPro-Core Authorized Pdf, New SnowPro-Core Exam Format

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Snowflake SnowPro-Core Certification Exam is a valuable credential for data professionals who work with Snowflake. It validates their skills and knowledge in Snowflake architecture, design, administration, and security. Preparing for the exam requires a solid understanding of Snowflake concepts and experience working with the platform in a production environment. Earning the certification can have many benefits for data professionals, including career advancement, increased earning potential, and access to the Snowflake community and resources.

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Snowflake SnowPro Core Certification Exam Sample Questions (Q161-Q166):

NEW QUESTION # 161
True or False: Snowflake's Global Services Layer gathers and maintains statistics on all columns in all micro-partitions.

  • A. False
  • B. True

Answer: A


NEW QUESTION # 162
Where would a Snowflake user find information about query activity from 90 days ago?

  • A. information__schema - query history_by_ses s i on view
  • B. account__usage.query__history__archive View
  • C. account__usage . query history view
  • D. information__schema . cruery_history view

Answer: B

Explanation:
To find information about query activity from 90 days ago, a Snowflake user should use the account_usage.query_history_archive view. This view is designed to provide access to historical query data beyond the default 14-day retention period found in the standard query_history view. It allows users to analyze and audit past query activities for up to 365 days after the date of execution, which includes the
90-day period mentioned.
References:
* [COF-C02] SnowPro Core Certification Exam Study Guide
* Snowflake Documentation on Account Usage Schema1


NEW QUESTION # 163
Which of the following are benefits of micro-partitioning? (Select TWO)

  • A. Rows are automatically stored in sorted order within micro-partitions
  • B. Micro-partitions cannot overlap in their range of values
  • C. Micro-partitions can reduce the amount of I/O from object storage to virtual warehouses
  • D. Micro-partitions can be defined on a schema-by-schema basis
  • E. Micro-partitions are immutable objects that support the use of Time Travel.

Answer: C,E

Explanation:
Micro-partitions in Snowflake are immutable objects, which means once they are written, they cannot be modified. This immutability supports the use of Time Travel, allowing users to access historical data within a defined period. Additionally, micro-partitions can significantly reduce the amount of I/O from object storage to virtual warehouses. This is because Snowflake's query optimizer can skip over micro-partitions that do not contain relevant data for a query, thus reducing the amount of data that needs to be scanned and transferred.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html


NEW QUESTION # 164
True or False: A Virtual Warehouse can be resized while suspended.

  • A. True
  • B. False

Answer: A

Explanation:
Virtual Warehouses in Snowflake can indeed be resized while they are suspended. Resizing a warehouse involves changing the number of compute resources (servers) allocated to it, which can be done to adjust performance and cost. When a warehouse is suspended, it is not currently running any queries, but its definition and metadata remain intact, allowing for modifications like resizing.
Reference:
https://docs.snowflake.com/en/user-guide/warehouses-tasks.html#effects-of-resizing-a-suspended-warehouse


NEW QUESTION # 165
Which Snowflake technique can be used to improve the performance of a query?

  • A. Clustering
  • B. Indexing
  • C. Using INDEX__HINTS
  • D. Fragmenting

Answer: A

Explanation:
Clustering is a technique used in Snowflake to improve the performance of queries. It involves organizing the data in a table into micro-partitions based on the values of one or more columns. This organization allows Snowflake to efficiently prune non-relevant micro-partitions during a query, which reduces the amount of data scanned and improves query performance.
References:
* [COF-C02] SnowPro Core Certification Exam Study Guide
* Snowflake Documentation on Clustering


NEW QUESTION # 166
......

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