Free Snowflake COF-C02 Practice Exam with Questions & Answers | Set: 7

When dealing with semi-structured data in Snowflake, and the specific types of operations to be performed on the data are not yet determined, Snowflake recommends using the VARIANT data type. The VARIANT type is highly flexible and capable of storing data in multiple formats, including JSON, AVRO, BSON, and more, within a single column. This flexibility allows users to perform various operations on the data, including querying and manipulation of nested data structures without predefined schemas.

References:

Snowflake Documentation: Semi-structured Data Types

When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.

References:

Snowflake Documentation: Using the Parquet File Format for Unloading Data

To change the existing file format type from CSV to JSON, the recommended way is to use the ALTER FILE FORMAT command with the SET TYPE=JSON clause. This alters the file format specification to use JSON instead of CSV. References: Based on my internal knowledge as of 2021.

Snowflake recommends creating objects with a role that has the necessary privileges and is not overly permissive. SYSADMIN is typically used for managing system-level objects and operations. Creating objects with a custom role and granting this role to SYSADMIN allows for more granular control and adherence to the principle of least privilege. References: Based on best practices for database object ownership and role management.

 To monitor data storage for individual tables, the commands and objects that can be used are ‘SHOW STORAGE BY TABLE;’ and the Information Schema view ‘TABLE_STORAGE_METRICS’. These tools provide detailed information about the storage utilization for tables. References: Snowflake Documentation

The search optimization service and automatic clustering are features that can improve the performance of queries returning a small subset of rows from a large table. The search optimization service is designed for low-latency point lookup queries, while automatic clustering organizes data in micro-partitions based on specific dimensions to reduce the amount of data scanned during queries.

In Snowflake, when queries begin to queue in a multi-cluster virtual warehouse, an additional compute cluster starts immediately if the warehouse is set to auto-scale mode. This mode allows Snowflake to automatically add or resume additional clusters as soon as the workload increases, and similarly, shut down or pause the additional clusters when the load decreases

 Once a database has been created in a consumer account from a share, the shared objects become accessible to users in that account. The shared objects are not transferred or copied; they remain in the provider’s account and are accessible to the consumer account

To access a JSON object in Snowflake, dot notation is used where the path to the object is specified after the column name containing the JSON data. Both lowercase and uppercase can be used for attribute names, so both “name” and “Name” are valid. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 An outer join, specifically a left outer join, will list all rows from the left table and match them with rows from the right table. If there is no match, the result will still include the row from the left table, with NULLs for columns from the right table. References: Based on general SQL knowledge as of 2021.

In the Admin area of Snowsight, users can analyze query performance, manage Snowflake warehouses, set up and view details about resource monitors, manage users and roles, and administer Snowflake accounts in their organization2.

 The OBJECT_CONSTRUCT function is used with the COPY INTO command to convert the rows in a relational table to a single VARIANT column, which can then be unloaded into a file. This is useful for transforming table data into a semi-structured JSON format

Snowflake’s documentation recommends producing the largest files possible for data loading, as larger files reduce the number of files to process and the overhead associated with handling many small files. This approach can maximize the load performance by leveraging Snowflake’s ability to ingest large files efficiently1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 The stream type that can be used for tracking the records in external tables is ‘External’. This type of stream is specifically designed to track changes in external tables

 For the ALLOWED VALUES tag property, the maximum number of possible string values for a single tag is 256. This allows for a wide range of values to be assigned to a tag when it is set on an object