Pass Guaranteed Quiz 2026 Snowflake SOL-C01: Snowflake Certified SnowPro Associate - Platform Certification–Professional Exam Success

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Snowflake SOL-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Identity and Data Access Management	15%	- Create a database, explore data, configure parameters, and transfer ownership 1. Understand context 2. Use database objects (INFORMATION_SCHEMA, PUBLIC SCHEMA) 3. Create and drop schemas 4. Run basic SQL commands (EXCLUDE, SELECT, LIMIT) 5. Transfer ownership - Define the roles that are used in Snowflake 1. Object access by role 2. Privileges 3. Role-Based Access Control (RBAC) 4. Explain role hierarchy 5. Role types
Topic 2: Data Loading and Virtual Warehouses	40%	- Explain how to use Snowflake Cortex LLM functions 1. TRANSLATE function 2. CLASSIFY_TEXT function 3. PARSE_DOCUMENT function 4. COMPLETE function - Create tables in Snowflake and load data into the tables 1. Use Snowsight to identify particular tables 2. View the table definition 3. Use INSERT statements to load data 4. Use COPY INTO statements 5. File format options 6. Use Snowsight to load data 7. Preview the table data - Explain how to work with unstructured data 1. Use of Pre-signed URLs 2. Directory tables (Enable, Use of SELECT statements) 3. Function argument
Topic 3: Data Protection and Data Sharing	10%	- Define Snowflake data sharing capabilities 1. Snowflake Marketplace (Search) - Outline continuous data protection with Snowflake 1. Time Travel 2. Cloning
Topic 4: Interacting with Snowflake and the Architecture	35%	- Outline key features and benefits of the Snowflake AI Data Cloud 1. Snowflake layers 2. Elastic compute 3. Elastic storage - Outline the key Snowflake user interfaces 1. Snowsight 2. Worksheets (Python, SQL) 3. Snowflake Notebooks - Describe how to use the Snowsight user interface 1. Query history 2. Data loading 3. Object browsers 4. Create objects (e.g., databases, schemas, stages) - Describe Snowflake objects and how they fit into the Snowflake hierarchy 1. Data types 2. Databases 3. Tables 4. Views 5. Schemas - Work with Snowflake Notebooks 1. Python variable substitution 2. Run code (SQL, Python, Cell execution status) 3. Notebook sessions 4. Visualize data using Streamlit

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q203-Q208):

NEW QUESTION # 203
A data engineer needs to create a table named 'EMPLOYEES in the `PUBLIC' schema of the database 'COMPANY DATA'. The table should store employee IDs as integers, names as strings, and hire dates as dates. Which of the following SQL statements correctly creates this table?

A. CREATE OR REPLACE TABLE COMPANY_DATA.PUBLIC.EMPLOYEES (EmployeelD
NUMBER, Name VARCHAR(255), HireDate DATE);
B. CREATE OR REPLACE TABLE COMPANY DATA.PUBLIC.EMPLOYEES (EmployeelD INT, Name VARCHAR(255), HireDate DATE);
C. CREATE OR REPLACE TABLE EMPLOYEES (EmployeelD NUMBER, Name VARCHAR,
HireDate DATE);
D. CREATE TABLE COMPANY_DATA.PUBLIC.EMPLOYEES (EmployeelD INT, Name STRING, HireDate DATE);
E. CREATE TABLE EMPLOYEES (EmployeelD INTEGER, Name VARCHAR(255), HireDate DATETIME);

Answer: B

Explanation:
The correct answer is E. It uses 'CREATE OR REPLACE to avoid errors if the table exists, specifies the full table name with database and schema ('COMPANY_DATA.PUBLIC.EMPLOYEES'), and uses appropriate data types: 'INT' for integers,
'VARCHAR(255)' for strings, and 'DATE for dates. Option A would cause errors if table already exists, Option B and C does not specify the database and schema. Option D uses NUMBER datatype.

NEW QUESTION # 204
You are working with a semi-structured JSON dataset containing information about products. The JSON structure is as follows:

You need to extract all the `name' attributes from the `attributes' array into a single comma- separated string for each product. Which of the following Snowflake SQL statements would achieve this?

Answer: E

Explanation:
The correct answer uses 'LATERAL FLATTEN' to unnest the 'attributes' array, and then uses
'LISTAGG' to concatenate the 'name' values into a comma-separated string. The 'WITHIN GROUP (ORDER BY seq)' clause ensures a consistent order. Option B is incorrect because it's trying to filter by key after flattening the 'attributes' array, which isn't how 'LATERAL FLATTEN' works. The correct column name 'name' needed to be referenced directly after the flatten command. Option A is syntactically incorrect for JSON array access. XMLAGG is not used for JSON. Option E incorrect because 'name' is a column after flatten is perfomed.

NEW QUESTION # 205
A data engineer is loading data into Snowflake from an external stage. They need to transform data during the load process. Which of the following is the MOST efficient and recommended approach for performing this transformation in Snowflake?

A. Use a Stored Procedure to read the data from the stage, transform it, and then insert it into the target table.
B. Use a COPY INTO statement with a SELECT statement that performs the transformations.
C. Create a View that transforms the data after loading it into a staging table.
D. Download the data from the stage, transform it using a Python script, and then upload it into Snowflake using the Snowflake connector.
E. Use Snowpipe with a transformation script running on a separate compute instance.

Answer: B

Explanation:
The COPY INTO statement with a SELECT statement provides the most efficient and recommended way to transform data during the load process. It leverages Snowflake's compute engine for transformations, avoiding the overhead of external scripts or stored procedures for simple transformations. Using views would transform the data on read, not during load.

NEW QUESTION # 206
What is the PRIMARY purpose of the use of the PARSE_DOCUMENT function in Snowflake?

A. To identify data that will benefit from the use of a directory table
B. To parse JSON data
C. To extract text from PDF files
D. To identify any Personally Identifiable Information (PII) in text

Answer: C

Explanation:
The PARSE_DOCUMENT function is part of Snowflake Cortex AI and is designed specifically to extract text, layout information, and structured elements from unstructured documents, especially PDFs. It supports OCR-based extraction for scanned files and layout-aware extraction to preserve tables, headings, and format structure.
Its purpose is not PII detection; Snowflake does not provide built-in automatic PII identification via PARSE_DOCUMENT. It does not identify candidate data for directory tables and is unrelated to JSON parsing-Snowflake uses PARSE_JSON for JSON data.
PARSE_DOCUMENT is primarily used for workflows such as contract analysis, invoice extraction, document classification, compliance automation, and downstream AI enrichment.

NEW QUESTION # 207
Which command is used to view the details of a file format object in Snowflake?

A. SHOW FILE FORMATS
B. ALTER FILE FORMAT
C. DESCRIBE FILE FORMAT
D. LIST FILE FORMAT

Answer: C

Explanation:
DESCRIBE FILE FORMAT <name> returns configuration settings such as FIELD_DELIMITER, TYPE, SKIP_HEADER, and COMPRESSION for the specified format.
SHOW FILE FORMATS only lists existing formats, not their internals.
LIST FILE FORMAT is invalid. ALTER modifies but does not display details.

NEW QUESTION # 208
......

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