Most Recent Snowflake COF-C02 Exam Questions & Answers

Prepare for the Snowflake SnowPro Core Certification Exam exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Snowflake COF-C02 exam and achieve success.

The questions for COF-C02 were last updated on Dec 21, 2024.

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Question No. 1

How does Snowflake utilize clustering information to improve query performance?

AIt prunes unnecessary micro-partitions based on clustering metadata.

BIt compresses the data within micro-partitions for faster querying.

CIt automatically allocates additional resources to improve query execution.

DIt organizes clustering information to speed-up data retrieval from storage

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Correct Answer: A

Snowflake utilizes clustering information to enhance query performance by pruning unnecessary micro-partitions.

Clustering Metadata: Snowflake stores clustering information for each micro-partition, which includes data range and distribution.

Pruning Micro-partitions: When a query is executed, Snowflake uses this clustering metadata to identify and eliminate micro-partitions that do not match the query criteria, thereby reducing the amount of data scanned and improving query performance.

Snowflake Documentation on Clustering

Snowflake Documentation on Micro-partition Pruning

Question No. 2

Which of the following is the Snowflake Account_Usage.Metering_History view used for?

AGathering the hourly credit usage for an account

BCompiling an account's average cloud services cost over the previous month

CSummarizing the throughput of Snowpipe costs for an account

DCalculating the funds left on an account's contract

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Correct Answer: A

The Snowflake Account_Usage.Metering_History view is used to gather the hourly credit usage for an account.This view provides details on the credits consumed by various services within Snowflake for the last 365 days1.

Question No. 3

In which use cases does Snowflake apply egress charges?

AData sharing within a specific region

BQuery result retrieval

CDatabase replication

DLoading data into Snowflake

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Correct Answer: C

Snowflake applies egress charges in the case of database replication when data is transferred out of a Snowflake region to another region or cloud provider. This is because the data transfer incurs costs associated with moving data across different networks. Egress charges are not applied for data sharing within the same region, query result retrieval, or loading data into Snowflake, as these actions do not involve data transfer across regions.

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Replication and Egress Charges1

Question No. 4

By definition, a secure view is exposed only to users with what privilege?

AIMPORT SHARE

BOWNERSHIP

CREFERENCES

DUSAGE

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Correct Answer: B

A secure view in Snowflake is exposed only to users with the OWNERSHIP privilege.This privilege ensures that only authorized users who own the view, or roles that include ownership, can access the secure view

Question No. 5

What virtual warehouse configuration should be used when processing a large number of complex queries?

AUse the auto-resume feature.

BRun the warehouse in auto-scale mode.

CIncrease the size of the warehouse.

DIncrease the number of warehouse clusters.

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Correct Answer: D

To handle a large number of complex queries, configuring the warehouse in auto-scale mode by increasing the number of warehouse clusters is recommended. This setup allows Snowflake to dynamically add clusters as demand increases, ensuring better performance and concurrency. Increasing the number of clusters provides scalability for concurrent users and heavy workloads, improving response times without impacting individual query performance.

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