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Snowflake SOL-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	Snowflake SnowPro Associate - Platform Certification
Exam Number:	SOL-C01
Exam Price:	$175 USD
Exam Format:	Multiple Select, Multiple Choice
Available Languages:	Japanese, English
Passing Score:	750/1000
Related Certifications:	SnowPro Advanced Architect SnowPro Advanced Data Engineer SnowPro Core Certification
Exam Duration:	115 minutes
Certificate Validity Period:	2 years
Real Exam Qty:	80-100
Recommended Training:	Snowflake Documentation Snowflake Learning & Training
Exam Registration:	Snowflake Certifications Portal
Sample Questions:	Snowflake SOL-C01 Sample Questions
Exam Way:	Online proctored exam via Snowflake certification platform or authorized testing provider.
Pre Condition:	No formal prerequisites required; basic knowledge of data warehousing and SQL recommended.
Official Syllabus URL:	https://www.snowflake.com/en/certifications/

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Snowflake SOL-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
Topic 3	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 4	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.

Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which of the following are benefits of using a multi-clustered warehouse in Snowflake? (Choose any 3 options)

A. Improved query performance during peak load
B. Automatic scaling of clusters based on query demand
C. Reduced storage costs
D. Increased compute capacity

Answer: A,B,D

Explanation:
A multi-cluster warehouse raises Snowflake's capability to handle concurrent workloads efficiently. When large numbers of users or queries hit the system, Snowflake can automatically start additional clusters (scaling out) to distribute workloads, reducing queuing and improving performance. As query demand drops, Snowflake scales in by shutting down clusters to conserve credits. This dynamic auto-scaling provides significantly increased compute capacity during peak usage periods and enhances responsiveness.
Importantly, multi-cluster warehouses affect only compute, not storage, so they do not reduce storage costs.
Their primary benefits revolve around performance stability, concurrency handling, workload isolation, and compute elasticity.

NEW QUESTION # 72
You have two tables, `employees' and `departments'. The `employees' table contains employee information, including 'employee_id' and The `departments' table contains department information, including and department_name'. You want to create a view that combines data from both tables, showing employee name and their respective department name. Which of the following approaches are valid when creating this view using Snowflake?

A. Create a view using a 'LATERAL FLATTEN' function to join the tables based on 'department_id'.
B. Create a materialized view using a standard 'JOIN' clause, and ensure the view is automatically refreshed at regular intervals.
C. Create a view using a standard 'JOIN' clause to combine the tables based on 'department_id'.
D. Create a secure view using a standard 'JOIN' clause to combine the tables, hiding the base table structures.
E. Create two separate views, one for 'employees' and one for 'departments', and then use a third view to combine them using a "UNION' clause.

Answer: C,D

Explanation:
Options A and C are valid. A standard view (Option A) is a simple and effective way to combine data from multiple tables. A secure view (Option C) provides an extra layer of security by hiding the underlying table structures. Option B is not optimal because Materialized views are designed for improving query performance with pre-computed result sets, not to hide base table structures.
Option D is incorrect; UNION is used to combine rows, not columns. Option E is incorrect; LATERAL FLATTEN is used for semi-structured data and is not applicable here.

NEW QUESTION # 73
To exclude certain columns from a SELECT query, you should:

A. Explicitly list the columns you want to include
B. Use a REMOVE function on the table
C. Use the OMIT clause
D. Use the EXCLUDE keyword

Answer: D

Explanation:
Snowflake supports theEXCLUDEkeyword to simplify queries when excluding certain columns from a SELECT * operation. SELECT * EXCLUDE (column1, column2) reduces verbosity and enhances maintainability, especially when table schemas evolve. Explicitly listing all columns is possible but inefficient. Snowflake does not support REMOVE functions for columns nor an OMIT clause. EXCLUDE is the correct and official mechanism.

NEW QUESTION # 74
A data engineer is tasked with creating a hierarchical structure for managing data access. They need to create a database named 'analytics db', a schema named 'reporting schema', and several views within the schema. These views will be used by different teams with varying levels of access. Which of the following SQL statements demonstrates the correct order and structure for creating these Snowflake objects, considering the object hierarchy?

A. CREATE DATABASE analytics_db; CREATE SCHEMA reporting_schema IN DATABASE analytics_db; CREATE VIEW AS SELECT FROM tablel;
B. CREATE SCHEMA reporting_schema; CREATE DATABASE analytics_db; CREATE VIEW reporting_schema.viewl AS SELECT FROM tablel ;
C. CREATE DATABASE analytics_db; CREATE SCHEMA CREATE VIEW AS SELECT FROM tablel;
D. CREATE VIEW AS SELECT FROM tablel ; CREATE SCHEMA reporting_schema; CREATE DATABASE analytics_db;
E. CREATE SCHEMA reporting_schema; CREATE DATABASE analytics_db; CREATE VIEW AS SELECT FROM tablel ;

Answer: A

Explanation:
Option D correctly reflects the Snowflake object hierarchy: Database Schema -> View. It first creates the database Then, it creates the schema `reporting_schema' within the `analytics_db' database. Finally, it creates the view `viewl within the analytics_db.reporting_schema' schema.
Other options are syntactically incorrect or do not follow the correct object hierarchy. Note the 'IN DATABASE' clause is required when specifying database name with Schema Create statement.
Option C is incorrect since you can't create a schema in a database like that.

NEW QUESTION # 75
Which column is returned when the FLATTEN table function is executed?

A. VALUE
B. ROWNUM
C. OUTPUT
D. LEVEL

Answer: A

Explanation:
The FLATTEN table function expands semi-structured data such as arrays or objects stored in VARIANT columns. Its output includes several metadata columns, and the most important among them isVALUE, which contains the extracted element from the array or object. VALUE is the column that developers typically join on or reference to access the flattened data.
In addition to VALUE, FLATTEN returns KEY (for object keys), INDEX (position in arrays), SEQ (internal ordering), and PATH (hierarchical reference path). These other columns support analytical or transformation logic, but VALUE remains the primary output used for downstream SQL.
The other listed options-OUTPUT, ROWNUM, and LEVEL-are not part of the FLATTEN function's output schema. ROWNUM and LEVEL may appear in other SQL contexts but not in FLATTEN results.
Therefore, the correct answer is VALUE.

NEW QUESTION # 76
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