Actual Snowflake SOL-C01 Exam Question For Quick Success

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Snowflake SOL-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.
Topic 2	Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
Topic 3	Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
Topic 4	Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q131-Q136):

NEW QUESTION # 131
A Snowflake administrator needs to determine the default collation setting for a newly created database. Which of the following methods can be used to identify the default collation of the database named 'MY DATABASE?

A. SELECT followed by SELECT collation FROM INFORMATION_SCHEMA.DATABASES WHERE database_name =
B. SELECT FROM WHERE option_name = 'COLLATION';
C. SHOW DATABASES LIKE 'MY DATABASE';
D. DESCRIBE DATABASE MY DATABASE;
E. SELECT DATABASE COLLATION FROM INFORMATION SCHEMA.DATABASES WHERE
DATABASE NAME = 'MY DATABASE';

Answer: D

Explanation:
'DESCRIBE DATABASE MY_DATABASE;' will show all properties of the database, including the default collation. 'SHOW DATABASES LIKE 'MY DATABASE';' will also show information about the database but does not guarantee displaying the default collation. Options C, D, and E use the INFORMATION_SCHEMA, which is useful, but option A gives direct and complete metadata.
'DATABASE_OPTIONS' table does not exists in information schema. The most efficient method and recommended way is to use `DESCRIBE DATABASE.

NEW QUESTION # 132
A data provider wants to share a secure view called 'SALES SUMMARY' from the 'RETAIL DATA' database with a consumer. The provider needs to ensure that the consumer can only query the view and cannot see the underlying tables. Which of the following steps are required to accomplish this? l. Create a secure view 'SALES SUMMARY'. II. Create a share. Ill. Grant
'SELECT' privilege on the 'SALES SUMMARY' view to the share. IU Grant 'USAGE privilege on the 'RETAIL DATA' database to the share. V. Grant `USAGE' privilege on the schema containing
`SALES SUMMARY' to the share.

A. l, II, and III only
B. II and III only
C. II, III, and V only
D. l, II, III, IV, and V
E. l, II, III, and IV only

Answer: D

Explanation:
To share a secure view, you need to create the view, create a share, grant SELECT on the view to the share, grant USAGE on the database to the share, and grant USAGE on the schema containing the view to the share. All steps l, II, III, IV and V are required.

NEW QUESTION # 133
What is the Snowsight object browser used for?

A. To create Snowsight worksheets
B. To manage virtual warehouses
C. To view and filter database objects
D. To modify security settings for objects

Answer: C

Explanation:
The Snowsight object browser is Snowflake's built-in visual interface for exploring and navigating all database objects within an account. It provides a hierarchical view across databases, schemas, and contained objects such as tables, views, stages, pipes, file formats, procedures, and functions. The key purpose of the object browser is to make it easy for users to view and filter database objects without needing SQL commands. Users can search objects by name, apply filters, preview table metadata, and inspect properties such as storage usage or row counts.
The object browser does not manage warehouses; warehouse management is done under the "Warehouses" section. It also does not create worksheets; worksheets are created in the "Worksheets" section. Additionally, the object browser does not modify security settings-permissions and grants are controlled through SQL (GRANT, REVOKE, SHOW GRANTS) or through the Access Control interfaces.
In summary, the object browser is a discovery and navigation layer, allowing users to efficiently find, inspect, and explore Snowflake objects.

NEW QUESTION # 134
You have a table named 'ORDERS' with a column 'ORDER DATE of data type DATE. You need to write a SQL query to retrieve all orders placed in the month of January 2023. Which of the following queries is the MOST efficient way to achieve this in Snowflake?

A. SELECT FROM ORDERS WHERE = 1 AND = 2023;
B. `sql SELECT FROM ORDERS WHERE ORDER DATE BETWEEN '2023-01-01' AND '2023-01-
31';
C. SELECT FROM ORDERS WHERE 'YYYY-MM') = '2023-01';
D. `sql SELECT FROM ORDERS WHERE DATE PART(month, ORDER DATE) = 1 AND DATE PART(year, ORDER DATE) = 2023;
E. SELECT FROM ORDERS WHERE ORDER_DATE LIKE '2023-01%'

Answer: B

Explanation:
Using a 'BETWEEN' operator with specific date values (Option B) is generally the most efficient way to filter data based on a date range in Snowflake. It allows Snowflake to utilize indexing (if available) and optimize the query execution plan. Options A and D, using functions like 'MONTH' and 'YEAR' or 'DATE_PART , prevent index usage and require Snowflake to evaluate the function for every row. Option C, converting the date to a string using `TO_CHAR , is also inefficient for the same reason. Option E, using `LIKE', is unsuitable for date comparisons and would not be performant, and could also potentially return incorrect results.

NEW QUESTION # 135
You are loading data from a CSV file stored in an Amazon S3 bucket into a Snowflake table named `CUSTOMER DATA. The CSV file contains a header row, and the data is comma- separated. The 'CUSTOMER DATA table has columns `customer_id', 'first_name', and 'email'.
You want to use a named file format object called `CSV FORMAT which you have already created. You also want to skip the header row and only load data where the column is not null.
Which of the following `COPY INTO' statement achieves this most efficiently and correctly?

Answer: A

Explanation:
Option D is the most correct because it efficiently copies the data from the stage, using a named file format, skips the header and sets error handling. The WHERE clause to filter null emails cannot be directly used within COPY INTO. Options A, B and C, is incorrect, as 'WHERE clause and 'SKIP_HEADER = 1' is not applicable as the way written in the option. E is incorrect as its using select statement and it cannot be used in 'COPY INTO'

NEW QUESTION # 136
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