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The SnowPro Core Certification is recognized by Snowflake as a validation of an individual's proficiency in using the platform. It is a globally recognized certification that demonstrates a professional's ability to work with Snowflake's cloud data platform to manage data at scale. By achieving this certification, individuals can enhance their career prospects and demonstrate their commitment to staying up-to-date with the latest technologies and best practices in data management.

Snowflake SnowPro Core Certification Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
What information is found within the Statistic output in the Query Profile Overview?

A. Most expensive nodes
B. Nodes by execution time
C. Operator tree
D. Table pruning

Answer: A

Explanation:
The Statistic output in the Query Profile Overview of Snowflake provides detailed insights into the performance of different parts of the query. Specifically, it highlights the "Most expensive nodes," which are the operations or steps within the query execution that consume the most resources, such as CPU and memory.
Identifying these nodes helps in pinpointing performance bottlenecks and optimizing query execution by focusing efforts on the most resource-intensive parts of the query.
References:
* Snowflake Documentation on Query Profile Overview: It details the components of the profile overview, emphasizing how to interpret the statistics section to improve query performance by understanding which nodes are most resource-intensive.

NEW QUESTION # 161
Which account__usage views are used to evaluate the details of dynamic data masking? (Select TWO)

A. QUERY_HISTORY
B. ACCESS_HISTORY
C. ROLES
D. RESOURCE_MONIT ORS
E. POLICY_REFERENCES

Answer: B,E

Explanation:
To evaluate the details of dynamic data masking, the POLICY_REFERENCES and ACCESS_HISTORY views in the account_usage schema are used. The POLICY_REFERENCES view provides information about the objects to which a masking policy is applied, and the ACCESS_HISTORY view contains details about access to the masked data, which can be used to audit and verify the application of dynamic data masking policies.
Reference:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Dynamic Data Masking1

NEW QUESTION # 162
Which Query Profile metrics will provide information that can be used to improve query performance?
(Select TWO).

A. Local disk IO
B. Spillage
C. Remote disk IO
D. Pruning
E. Synchronization

Answer: C,D

Explanation:
Two key metrics in Snowflake's Query Profile that provide insights for performance improvement are:
* Remote Disk IO: This measures the time the query spends waiting on remote disk access, indicating potential performance issues related to I/O bottlenecks.
* Pruning: This metric reflects how effectively Snowflake's micro-partition pruning is reducing the data scanned. Better pruning (more partitions excluded) leads to faster query performance, as fewer micro- partitions need to be processed.
These metrics are essential for identifying and addressing inefficiencies in data retrieval and storage access, optimizing overall query performance.

NEW QUESTION # 163
Which data types does Snowflake support when querying semi-structured data? (Select TWO)

A. BLOB
B. VARCHAR
C. XML
D. VARIANT
E. ARRAY

Answer: D,E

Explanation:
Explanation
https://docs.snowflake.com/en/user-guide/semistructured-intro.html#label-loading-semi-structured-data A VARIANT stores semi-structured data in Snowflake. It can store a value of any other type, including OBJECT and ARRAY.
The maximum length of a VARIANT is 16 MB.
A Snowflake ARRAY is similar to an array in many other programming languages. An ARRAY contains 0 or more pieces of data. Each element is accessed by specifying its position in the array.

NEW QUESTION # 164
How would you determine the size of the virtual warehouse used for a task?

A. If using the stored procedure to execute multiple SQL statements, it's best to test run the stored procedure separately to size the compute resource first
B. Root task may be executed concurrently (i.e. multiple instances), it is recommended to leave some margins in the execution window to avoid missing instances of execution
C. Since task infrastructure is based on running the task body on schedule, it's recommended to configure the virtual warehouse for automatic concurrency handling using Multi-cluster warehouse (MCW) to match the task schedule
D. Querying (select) the size of the stream content would help determine the warehouse size. For example, if querying large stream content, use a larger warehouse size

Answer: C

Explanation:
The size of the virtual warehouse for a task can be configured to handle concurrency automatically using a Multi-cluster warehouse (MCW). This is because tasks are designed to run their body on a schedule, and MCW allows for scaling compute resources to match the task's execution needs without manual intervention. References: [COF-C02] SnowPro Core Certification Exam Study Guide

NEW QUESTION # 165
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