Replace SnowPro Core Certification Exam (COF-C02) COF-C02 Exam Price

NEW QUESTION 1

Which object can be used with Secure Data Sharing?

• A. View
• B. Materialized view
• C. External table
• D. User-Defined Function (UDF)

Answer: A

Explanation:
Views can be used with Secure Data Sharing in Snowflake. Materialized views, external tables, and UDFs are not typically shared directly for security and performance reasons2.

NEW QUESTION 2

Which type of charts are supported by Snowsight? {Select TWO)

• A. Flowcharts
• B. Gantt charts
• C. Line charts
• D. Pie charts
• E. Scatterplots

Answer: DE

NEW QUESTION 3

What operations can be performed while loading a simple CSV file into a Snowflake table using the COPY INTO command? (Select TWO).

• A. Performing aggregate calculations
• B. Reordering the columns
• C. Grouping by operations
• D. Converting the datatypes
• E. Selecting the first few rows

Answer: BD

Explanation:
When loading a simple CSV file into a Snowflake table using the COPY INTO command, you can perform various transformations and adjustments on the data as part of the loading process. Specifically, you can:
✑ B. Reorder the columns: Specify the order of columns in the COPY INTO command to match the structure of the target table if it differs from the order of columns in the source CSV file.
✑ D. Convert the datatypes: Explicitly convert the datatypes of the data being loaded to match the datatypes of the columns in the target table. This can benecessary when the source data's format does not match the target table's expected datatype.
References:
✑ Snowflake Documentation: Using the COPY INTO Command for Data Loading

NEW QUESTION 4

Which feature is only available in the Enterprise or higher editions of Snowflake?

• A. Column-level security
• B. SOC 2 type II certification
• C. Multi-factor Authentication (MFA)
• D. Object-level access control

Answer: A

Explanation:
Column-level security is a feature that allows fine-grained control over access to specific columns within a table. This is particularly useful for managing sensitive data and ensuring that only authorized users can view or manipulate certain pieces of information. According to my last update, this feature was available in the Enterprise Edition or higher editions of Snowflake.
References: Based on my internal data as of 2021, column-level security is an advanced feature typically reserved for higher-tiered editions like the Enterprise Edition in data warehousing solutions such as Snowflake.
https://docs.snowflake.com/en/user-guide/intro-editions.html

NEW QUESTION 5

Which Snowflake edition enables data sharing only through Snowflake Support?

• A. Virtual Private Snowflake
• B. Business Critical
• C. Enterprise
• D. Standard

Answer: A

Explanation:
The Snowflake edition that enables data sharing only through Snowflake Support is the Virtual Private Snowflake (VPS). By default, VPS does not permit data sharing outside of the VPS environment, but it can be enabled through Snowflake Support4.

NEW QUESTION 6

A user has unloaded data from a Snowflake table to an external stage.
Which command can be used to verify if data has been uploaded to the external stage named my_stage?

• A. view @my_stage
• B. list @my_stage
• C. show @my_stage
• D. display @my_stage

Answer: B

Explanation:
The list @my_stage command in Snowflake can be used to verify if data has been uploaded to an external stage named my_stage. This command provides a list of files that are present in the specified stage2.

NEW QUESTION 7

Which Snowflake data type is used to store JSON key value pairs?

• A. TEXT
• B. BINARY
• C. STRING
• D. VARIANT

Answer: D

Explanation:
The VARIANT data type in Snowflake is used to store JSON key-value pairs along with other semi-structured data formats like AVRO, BSON, and XML. The VARIANT data type allows for flexible and dynamic data structures within a single column, accommodating complex and nested data. This data type is crucial for handling semi- structured data in Snowflake, enabling users to perform SQL operations on JSON objects and arrays directly.
References:
✑ Snowflake Documentation: Semi-structured Data Types

NEW QUESTION 8

During periods of warehouse contention which parameter controls the maximum length of time a warehouse will hold a query for processing?

• A. STATEMENT_TIMEOUT IN SECONDS
• B. STATEMENT_QUEUED_TIMEOUT_IN_SECONDS
• C. MAX_CONCURRENCY LEVEL
• D. QUERY_TIMEOUT_IN_SECONDS

Answer: B

Explanation:
The parameter STATEMENT_QUEUED_TIMEOUT_IN_SECONDS sets the limit for a query to wait in the queue in order to get its chance of running on the warehouse. The query will quit after reaching this limit. By default, the value of this parameter is 0 which mean the queries will wait indefinitely in the waiting queue https://community.snowflake.com/s/article/Warehouse-Concurrency-and-Statement- Timeout- Parameters#:~:text=The%20parameter%20STATEMENT_QUEUED_TIMEOUT_IN_SECO NDS%20sets%20the,indefinitely%20in%20the%20waiting%20queue.

NEW QUESTION 9

Which database objects can be shared with the Snowflake secure data sharing feature? (Choose two.)

• A. Files
• B. External tables
• C. Secure User-Defined Functions (UDFs)
• D. Sequences
• E. Streams

Answer: BC

Explanation:
Snowflake??s secure data sharing feature allows sharing of certain database objects with other Snowflake accounts. Among the options provided, external tables and secure UDFs can be shared

NEW QUESTION 10

What is the MINIMUM edition of Snowflake that is required to use a SCIM security integration?

• A. Business Critical Edition
• B. Standard Edition
• C. Virtual Private Snowflake (VPS)
• D. Enterprise Edition

Answer: D

Explanation:
The minimum edition of Snowflake required to use a SCIM security integration is the Enterprise Edition. SCIM integrations are used for automated management of user identities and groups, and this feature is available starting from the Enterprise Edition of Snowflake. References: [COF-C02] SnowPro Core Certification Exam Study Guide

NEW QUESTION 11

Which Snowflake partner specializes in data catalog solutions?

• A. Alation
• B. DataRobot
• C. dbt
• D. Tableau

Answer: A

Explanation:
Alation is known for specializing in data catalog solutions and is a partner of Snowflake. Data catalog solutions are essential for organizations to effectively manage their metadata and make it easily accessible and understandable for users, which aligns with the capabilities provided by Alation.
References:
✑ [COF-C02] SnowPro Core Certification Exam Study Guide
✑ Snowflake??s official documentation and partner listings

NEW QUESTION 12

Which semi-structured file format is a compressed, efficient, columnar data representation?

• A. Avro
• B. JSON
• C. TSV
• D. Parquet

Answer: D

Explanation:
Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row- oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.References:Apache Parquet Documentation

NEW QUESTION 13

Which data type can store more than one type of data structure?

• A. JSON
• B. BINARY
• C. VARCHAR
• D. VARIANT

Answer: D

Explanation:
The VARIANT data type in Snowflake can store multiple types of data structures, as it is designed to hold semi-structured data. It can contain any other data type, including OBJECT and ARRAY, which allows it to represent various data structures

NEW QUESTION 14

Which function can be used with the copy into <location> statement to convent rows from a relational table to a single variant column, and to unload rows into a JSON file?

• A. FLATTEN
• B. OBJECT_AS
• C. OBJECT_CONSTRUCT
• D. TO VARIANT

Answer: D

Explanation:
The correct function to use with theCOPY INTO <location>statement to convert rows from a relational table into a single variant column and to unload rows into a JSON file isTO VARIANT. TheTO VARIANTfunction is used to explicitly convert a value of any supported data type into a VARIANT data type. This is particularly useful when needing to aggregate multiple columns or complex data structures into a single JSON-formatted string, which can then be unloaded into a file.
In the context of unloading data, theCOPY INTO <location>statement combined withTO VARIANTenables the conversion of structured data from Snowflake tables into a semi- structured VARIANT format, typically JSON, which can then be efficiently exported and stored. This approach is often utilized for data integration scenarios, backups, or when data needs to be shared in a format that is easily consumed by various applications or services that support JSON.
References:
✑ Snowflake Documentation on Data Unloading: Unloading Data
✑ Snowflake Documentation on VARIANT Data Type: Working with JSON

NEW QUESTION 15

How would you determine the size of the virtual warehouse used for a task?

• A. Root task may be executed concurrently (i.
• B. multiple instances), it is recommended to leave some margins in the execution window to avoid missing instances of execution
• C. Querying(select)the size of the stream content would help determine the warehouse siz
• D. For example, if querying large stream content, use a larger warehouse size
• E. 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
• F. 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

Answer: D

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 16

What happens to the shared objects for users in a consumer account from a share, once a database has been created in that account?

• A. The shared objects are transferred.
• B. The shared objects are copied.
• C. The shared objects become accessible.
• D. The shared objects can be re-shared.

Answer: C

Explanation:
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

NEW QUESTION 17

What is the MOST performant file format for loading data in Snowflake?

• A. CSV (Unzipped)
• B. Parquet
• C. CSV (Gzipped)
• D. ORC

Answer: B

Explanation:
Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries on large datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.
References:
✑ [COF-C02] SnowPro Core Certification Exam Study Guide
✑ Snowflake Documentation on Data Loading1

NEW QUESTION 18

Which function returns an integer between 0 and 100 when used to calculate the similarity of two strings?

• A. APPROXIMATE_SIMILARITY
• B. JAROWINKLER_SIMILARITY
• C. APPROXIMATE_JACCARD_INDEX
• D. MINHASH COMBINE

Answer: B

Explanation:
TheJAROWINKLER_SIMILARITYfunction in Snowflake returns an integer between 0 and 100, indicating the similarity of two strings based on the Jaro-Winkler similarity algorithm. This function is useful for comparing strings and determining how closely they match each other.
✑ Understanding JAROWINKLER_SIMILARITY:The Jaro-Winkler similarity metric is
a measure of similarity between two strings. The score is a number between 0 and 100, where 100 indicates an exact match and lower scores indicate less similarity.
✑ Usage Example:To compare two strings and get their similarity score, you can
use: SELECTJAROWINKLER_SIMILARITY('string1','string2')ASsimilarity_score;
✑ Application Scenarios:This function is particularly useful in data cleaning,
matching, and deduplication tasks where you need to identify similar but not identical strings, such as names, addresses, or product titles.
Reference:For more detailed information on theJAROWINKLER_SIMILARITYfunction and its usage, refer to the Snowflake documentation on string functions: https://docs.snowflake.com/en/sql-reference/functions/jarowinkler_similarity.html

NEW QUESTION 19
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