Practice Free SOL-C01 Exam Online Questions
Question #41
How can you search for listings on Snowflake Marketplace? (Choose any 2 options)
- A . Browsing categories and tags
- B . Contact Snowflake Support
- C . User Universal Search
- D . Using the search bar at the top of the marketplace
Correct Answer: A, C, D
A, C, D
Explanation:
Snowflake Marketplace provides multiple intuitive discovery mechanisms to help users explore available listings efficiently. The search bar at the top of the Marketplace is a primary entry point, enabling keyword-based discovery across providers, industries, and dataset types. This facilitates targeted searching for specific topics or data categories.
Universal Search, available in Snowsight, extends search beyond databases and worksheets to include Marketplace listings. This unified search experience allows users to locate datasets without navigating through menus, improving productivity and discoverability.
Browsing categories and tags supports exploratory search by grouping listings under predefined themes such as financial data, geospatial data, demographics, and more. These tags promote structured navigation, especially useful when users are exploring rather than seeking something specific.
Contacting Snowflake Support is not required for accessing or discovering listings and is irrelevant to Marketplace search workflows.
A, C, D
Explanation:
Snowflake Marketplace provides multiple intuitive discovery mechanisms to help users explore available listings efficiently. The search bar at the top of the Marketplace is a primary entry point, enabling keyword-based discovery across providers, industries, and dataset types. This facilitates targeted searching for specific topics or data categories.
Universal Search, available in Snowsight, extends search beyond databases and worksheets to include Marketplace listings. This unified search experience allows users to locate datasets without navigating through menus, improving productivity and discoverability.
Browsing categories and tags supports exploratory search by grouping listings under predefined themes such as financial data, geospatial data, demographics, and more. These tags promote structured navigation, especially useful when users are exploring rather than seeking something specific.
Contacting Snowflake Support is not required for accessing or discovering listings and is irrelevant to Marketplace search workflows.
Question #42
What is the primary purpose of the LIMIT clause in a Snowflake query?
- A . To sort the result set
- B . To filter the result set
- C . To restrict the number of rows returned
- D . To group the result set
Correct Answer: C
C
Explanation:
The LIMIT clause limits the number of rows Snowflake returns from a query. It is commonly used to preview datasets, improve performance when exploring large tables, and paginate results. LIMIT may be combined with OFFSET to skip a number of rows. Sorting is performed with ORDER BY, filtering with WHERE, and grouping with GROUP BY―not with LIMIT. Therefore, restricting returned rows is the primary purpose of LIMIT.
C
Explanation:
The LIMIT clause limits the number of rows Snowflake returns from a query. It is commonly used to preview datasets, improve performance when exploring large tables, and paginate results. LIMIT may be combined with OFFSET to skip a number of rows. Sorting is performed with ORDER BY, filtering with WHERE, and grouping with GROUP BY―not with LIMIT. Therefore, restricting returned rows is the primary purpose of LIMIT.
Question #43
What types of worksheets can be created in Snowsight? (Select TWO).
- A . SQL
- B . JavaScript
- C . Scala
- D . Java
- E . Python
Correct Answer: A, E
A, E
Explanation:
Snowsight supports two worksheet types: SQL worksheets and Python worksheets. SQL worksheets allow users to execute queries, create objects, and perform data analysis using ANSI SQL and Snowflake-specific extensions. Python worksheets, powered by Snowpark, allow users to write Python code that interacts directly with Snowflake tables, data frames, and machine learning workflows.
Java, Scala, and JavaScript are supported via Snowpark APIs or UDF development, but they cannot be used as worksheet languages. Worksheets are designed for interactive analysis, visualization, and iterative development, with native runtimes only for SQL and Python.
Thus, only SQL and Python worksheets can be created within Snowsight.
A, E
Explanation:
Snowsight supports two worksheet types: SQL worksheets and Python worksheets. SQL worksheets allow users to execute queries, create objects, and perform data analysis using ANSI SQL and Snowflake-specific extensions. Python worksheets, powered by Snowpark, allow users to write Python code that interacts directly with Snowflake tables, data frames, and machine learning workflows.
Java, Scala, and JavaScript are supported via Snowpark APIs or UDF development, but they cannot be used as worksheet languages. Worksheets are designed for interactive analysis, visualization, and iterative development, with native runtimes only for SQL and Python.
Thus, only SQL and Python worksheets can be created within Snowsight.
Question #44
Which command will create a named internal stage?
- A . CREATE @STAGE
- B . CREATE NAMED STAGE
- C . CREATE STAGE
- D . CREATE INTERNAL STAGE
Correct Answer: C
C
Explanation:
CREATE STAGE is the correct Snowflake DDL command for creating a named internal stage. Stages hold files for loading/unloading. The command defaults to internal storage unless external parameters are provided.
Other options are not valid Snowflake syntax.
C
Explanation:
CREATE STAGE is the correct Snowflake DDL command for creating a named internal stage. Stages hold files for loading/unloading. The command defaults to internal storage unless external parameters are provided.
Other options are not valid Snowflake syntax.
Question #45
Which SQL function is used to parse a string as JSON data within a Snowflake query?
- A . CONVERT_JSON()
- B . TO_JSON()
- C . PARSE_JSON()
- D . EXTRACT_JSON()
Correct Answer: C
C
Explanation:
The PARSE_JSON()function converts a valid JSON string into a VARIANT value. This allows Snowflake to store and query nested, hierarchical data using dot and bracket notation.
Example:
SELECT PARSE_JSON(‘{"name": "John", "age": 30}’) AS data;
After parsing, fields can be accessed like:
data: name or data[‘age’]
Incorrect options:
TO_JSON()converts VARIANT to a JSON string (opposite direction).
CONVERT_JSON and EXTRACT_JSON are not Snowflake functions.
PARSE_JSON is essential for dynamically loading, transforming, or analyzing JSON content from files, streams, or external applications.
C
Explanation:
The PARSE_JSON()function converts a valid JSON string into a VARIANT value. This allows Snowflake to store and query nested, hierarchical data using dot and bracket notation.
Example:
SELECT PARSE_JSON(‘{"name": "John", "age": 30}’) AS data;
After parsing, fields can be accessed like:
data: name or data[‘age’]
Incorrect options:
TO_JSON()converts VARIANT to a JSON string (opposite direction).
CONVERT_JSON and EXTRACT_JSON are not Snowflake functions.
PARSE_JSON is essential for dynamically loading, transforming, or analyzing JSON content from files, streams, or external applications.
Question #46
What are the steps to create a new schema in Snowsight?
- A . Data >> Add Data >> Create Schema
- B . Data >> Create Schema
- C . Data >> Database >> Create Schema
- D . Data Product >> Create Schema
Correct Answer: C
C
Explanation:
To create a new schema in Snowsight, users must follow the hierarchical navigation of Snowflake’s object structure. The correct sequence is:
Data → Database → Create Schema
Steps:
Open the Datatab in the left navigation.
Select a Database where the schema will be created.
ChooseCreate → Schema.
Enter the schema name and (optionally) description.
ClickCreate.
Incorrect options:
“Add Data” is used for loading files, not creating schema objects.
“Data → Create Schema” is incomplete because Snowflake must know which database the schema belongs to.
“Data Product” is unrelated to object creation.
Thus, Snowflake requires explicit database context before schema creation.
C
Explanation:
To create a new schema in Snowsight, users must follow the hierarchical navigation of Snowflake’s object structure. The correct sequence is:
Data → Database → Create Schema
Steps:
Open the Datatab in the left navigation.
Select a Database where the schema will be created.
ChooseCreate → Schema.
Enter the schema name and (optionally) description.
ClickCreate.
Incorrect options:
“Add Data” is used for loading files, not creating schema objects.
“Data → Create Schema” is incomplete because Snowflake must know which database the schema belongs to.
“Data Product” is unrelated to object creation.
Thus, Snowflake requires explicit database context before schema creation.
Question #47
Which of the following are benefits of using a multi-cluster warehouse in Snowflake? (Choose any 2 options)
- A . Reduced storage costs
- B . Improved concurrency for handling many simultaneous queries
- C . Automatic scaling to meet varying query loads
- D . Faster data loading
Correct Answer: B, C
B, C
Explanation:
A multi-cluster warehouse provides Snowflake’s highest level of concurrency support by allocating multiple compute clusters behind a single warehouse definition. It automatically adds clusters during peak workload to eliminate query queuing and maintain stable performance for dashboards, BI tools, and multi-user environments.
Improved concurrency is the primary benefit―large numbers of simultaneous queries are distributed across clusters.
Automatic scaling (scaling out and in) ensures compute cost efficiency, as Snowflake adds clusters only when needed and removes them when load decreases.
Incorrect options:
Storage costs are unaffected because compute and storage are independent.
Faster data loading may occur if warehouse size increases, but multi-cluster design is driven by concurrency, not ingestion performance.
The design enables smooth operation in high-demand enterprise environments.
B, C
Explanation:
A multi-cluster warehouse provides Snowflake’s highest level of concurrency support by allocating multiple compute clusters behind a single warehouse definition. It automatically adds clusters during peak workload to eliminate query queuing and maintain stable performance for dashboards, BI tools, and multi-user environments.
Improved concurrency is the primary benefit―large numbers of simultaneous queries are distributed across clusters.
Automatic scaling (scaling out and in) ensures compute cost efficiency, as Snowflake adds clusters only when needed and removes them when load decreases.
Incorrect options:
Storage costs are unaffected because compute and storage are independent.
Faster data loading may occur if warehouse size increases, but multi-cluster design is driven by concurrency, not ingestion performance.
The design enables smooth operation in high-demand enterprise environments.
Question #47
Which of the following are benefits of using a multi-cluster warehouse in Snowflake? (Choose any 2 options)
- A . Reduced storage costs
- B . Improved concurrency for handling many simultaneous queries
- C . Automatic scaling to meet varying query loads
- D . Faster data loading
Correct Answer: B, C
B, C
Explanation:
A multi-cluster warehouse provides Snowflake’s highest level of concurrency support by allocating multiple compute clusters behind a single warehouse definition. It automatically adds clusters during peak workload to eliminate query queuing and maintain stable performance for dashboards, BI tools, and multi-user environments.
Improved concurrency is the primary benefit―large numbers of simultaneous queries are distributed across clusters.
Automatic scaling (scaling out and in) ensures compute cost efficiency, as Snowflake adds clusters only when needed and removes them when load decreases.
Incorrect options:
Storage costs are unaffected because compute and storage are independent.
Faster data loading may occur if warehouse size increases, but multi-cluster design is driven by concurrency, not ingestion performance.
The design enables smooth operation in high-demand enterprise environments.
B, C
Explanation:
A multi-cluster warehouse provides Snowflake’s highest level of concurrency support by allocating multiple compute clusters behind a single warehouse definition. It automatically adds clusters during peak workload to eliminate query queuing and maintain stable performance for dashboards, BI tools, and multi-user environments.
Improved concurrency is the primary benefit―large numbers of simultaneous queries are distributed across clusters.
Automatic scaling (scaling out and in) ensures compute cost efficiency, as Snowflake adds clusters only when needed and removes them when load decreases.
Incorrect options:
Storage costs are unaffected because compute and storage are independent.
Faster data loading may occur if warehouse size increases, but multi-cluster design is driven by concurrency, not ingestion performance.
The design enables smooth operation in high-demand enterprise environments.
Question #49
Which layer of Snowflake’s architecture provides security features like encryption and authentication?
- A . Compute Layer
- B . Data Storage Layer
- C . Cloud Services Layer
- D . Query Processing Layer
Correct Answer: C
C
Explanation:
The Cloud Services Layer is responsible for Snowflake’s global security, coordination, and metadata management. It orchestrates all authentication and authorization processes, including username/password login, MFA, OAuth, SSO, key-pair authentication, and integration with enterprise identity providers. It also manages role-based access control (RBAC), privilege enforcement, session management, and secure query orchestration.
This layer centralizes encryption policy enforcement, including the management of Snowflake-managed and customer-managed encryption keys (Tri-Secret Secure). While all data stored and transmitted in Snowflake is encrypted by default, it is the Cloud Services Layer that handles the key lifecycle, rotation, and hierarchy.
The Compute Layer only runs queries.
The Storage Layer keeps compressed, encrypted columnar data but does not manage security controls.
The Query Processing Layer executes SQL but does not manage authentication or RBAC.
Thus, the Cloud Services Layer is the authoritative control plane for Snowflake security.
C
Explanation:
The Cloud Services Layer is responsible for Snowflake’s global security, coordination, and metadata management. It orchestrates all authentication and authorization processes, including username/password login, MFA, OAuth, SSO, key-pair authentication, and integration with enterprise identity providers. It also manages role-based access control (RBAC), privilege enforcement, session management, and secure query orchestration.
This layer centralizes encryption policy enforcement, including the management of Snowflake-managed and customer-managed encryption keys (Tri-Secret Secure). While all data stored and transmitted in Snowflake is encrypted by default, it is the Cloud Services Layer that handles the key lifecycle, rotation, and hierarchy.
The Compute Layer only runs queries.
The Storage Layer keeps compressed, encrypted columnar data but does not manage security controls.
The Query Processing Layer executes SQL but does not manage authentication or RBAC.
Thus, the Cloud Services Layer is the authoritative control plane for Snowflake security.
Question #50
What is the name of Snowflake’s default web-based interface?
- A . SnowSQL
- B . Snowpark
- C . Snowsight
- D . Snowflake CLI
Correct Answer: C
C
Explanation:
Snowsight is the default web-based interface for Snowflake. It provides a graphical, browser-based environment to write and run SQL and Python, manage and explore database objects, create visualizations and dashboards, monitor query and warehouse activity, and collaborate via worksheets. It has replaced the Classic Console as the default UI for new Snowflake accounts.
SnowSQL is a command-line client used to connect to Snowflake from a terminal and execute SQL statements. Snowpark is a developer framework that supports writing data applications in languages like Python, Java, and Scala, but it is not a UI. “Snowflake CLI” refers to command-line tooling and automation utilities, again not a graphical web interface.
C
Explanation:
Snowsight is the default web-based interface for Snowflake. It provides a graphical, browser-based environment to write and run SQL and Python, manage and explore database objects, create visualizations and dashboards, monitor query and warehouse activity, and collaborate via worksheets. It has replaced the Classic Console as the default UI for new Snowflake accounts.
SnowSQL is a command-line client used to connect to Snowflake from a terminal and execute SQL statements. Snowpark is a developer framework that supports writing data applications in languages like Python, Java, and Scala, but it is not a UI. “Snowflake CLI” refers to command-line tooling and automation utilities, again not a graphical web interface.