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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	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 3	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 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 (Q76-Q81):

NEW QUESTION # 76
A developer wants to create a stored procedure in Snowflake using the web UI worksheet. The stored procedure needs to accept a VARIANT data type as input, parse a JSON string within the VARIANT, and insert specific values from the JSON into a table. Which of the following code snippets, when placed inside the stored procedure's body, correctly parses the JSON and inserts the 'name' and 'age' from the VARIANT 'json_data' into the 'employees' table?

Answer: E

Explanation:
Option B is the correct answer. Snowflake uses the colon operator to access elements within a VARIANT data type representing a JSON object. The and are necessary to explicitly cast the extracted JSON values to the appropriate data types for the `employees' table. Options A, C, and D use incorrect syntax for accessing JSON elements within a VARIANT in Snowflake. Option E uses STRING and NIJMBER which may be less precise than VARCHAR and INT, although would function, Option B is best practice.

NEW QUESTION # 77
You are tasked with loading data from an external stage containing gzipped CSV files into a Snowflake table. The CSV files have a header row that you want to skip during the load process.
Additionally, you need to transform the data during the load, specifically concatenating two columns (FIRST NAME and LAST NAME) into a new column called FULL NAME. Which combination of the following COPY INTO options will accomplish this task? Choose all that apply.

A. FILE_FORMAT = (TYPE = CSV, SKIP_HEADER = 1 , COMPRESSION = 'GZIP')
B. TRANSFORM = (FULL_NAME = FIRST_NAME II ' ' II LAST_NAME)
C. ON ERROR = 'SKIP FILE'
D. TRANSFORM AS SELECT $1, $2, $3 II ' II $4 FROM @mystage/data.csv
E. FILE FORMAT = (TYPE = CSV, SKIP_HEADER = 1, COMPRESSION = 'GZIP'), TRANSFORM AS SELECT $1, $2, $3 II " II $4 FROM @mystage/data.csv

Answer: A,D

Explanation:
Option A sets the FILE FORMAT correctly specifying the file type as CSV, skipping the header row (SKIP HEADER = 1), and specifying the compression type as GZIP. Option D correctly transforms the data by selecting all columns and concatenating the columns. This option assumes the file format is already defined in the COPY INTO statement. The @mystage/data.csv after FROM should only include name of the stage name and not the filename. However, that is not part of copy options but part of FROM Statement. The TRANSFORMS parameters that are part of copy options are deprecated.

NEW QUESTION # 78
Which SQL keyword is used to constrain the number of rows returned by a query in Snowflake? (Select TWO)

A. LIMIT
B. TOP
C. ROW_NUMBER
D. SELECT
E. GET

Answer: A,B

Explanation:
Snowflake supports bothTOPandLIMITas valid SQL mechanisms for restricting the number of rows returned by a query. They function similarly but are used in different syntactic positions.
LIMITappears at the end of the query:
SELECT * FROM my_table LIMIT 10;
This tells Snowflake to return only the specified number of rows.
TOPis used in the SELECT clause:
SELECT TOP 10 * FROM my_table;
Both approaches are equivalent in functionality and are supported to maintain compatibility with ANSI SQL and other SQL dialects.
The other options are not correct row-limiting mechanisms:
*SELECTis a required keyword but does not limit rows on its own.
*GETis not a SQL keyword for row restriction; GET is used for downloading files from internal stages.
*ROW_NUMBERis a window function used for ranking rows, not constraining row count.
Therefore, TOP and LIMIT are the only correct SQL keywords in Snowflake for controlling how many rows are returned by a query.

NEW QUESTION # 79
You are tasked with classifying a large dataset of customer support tickets using Snowflake Cortex's CLASSIFY TEXT function. You need to ensure optimal performance and cost- effectiveness. Which of the following strategies would be MOST effective?

A. Disable auto-suspension on your warehouse to keep it running continuously while classifying the data. This will prevent any potential startup delays.
B. Increase the parameter to a very high value to prevent query failures due to the long processing time of CLASSIFY TEXT.
C. Load all customer support tickets into a single very wide row and classify that single row using CLASSIFY _ TEXT. This minimizes the number of rows processed.
D. Profile the execution time of CLASSIFY _ TEXT with different warehouse sizes and choose the smallest warehouse that meets your performance requirements. Store the results in a materialized view.
E. Use a very large warehouse (e.g., X-LARGE) to execute the CLASSIFY _ TEXT function, regardless of the data volume. Larger warehouses always provide faster processing.

Answer: D

Explanation:
Option C is the most sensible approach. Profiling with different warehouse sizes allows you to determine the optimal balance between performance and cost. Using a materialized view ensures that the classification is only performed once, and subsequent queries are faster. Option A is wasteful, as a very large warehouse may not be necessary. Option B addresses timeout issues but doesn't optimize performance or cost. Option D is also wasteful and can lead to unnecessary costs. Option E is illogical as CLASSIFY _ TEXT is applied to text and not a representation of the entire dataset in a single row.

NEW QUESTION # 80
Which statement is true about Snowflake Data Exchange? (Choose any 2 options)

A. It allows organizations to securely share live, governed data
B. It requires complex ETL processes to transfer data
C. It supports data sharing between different regions and cloud providers
D. It is limited to internal data sharing only

Answer: A,C

Explanation:
Snowflake Data Exchangeprovides governed, real-time data collaboration between Snowflake accounts. It enables providers to publishlive datasetswhile consumers query that data without copying or moving it.
Because Snowflake uses secure data sharing primitives at the metadata layer,no ETL pipelinesor data duplication are required.
A key advantage is support forcross-region and cross-cloud sharing, allowing collaboration across AWS, Azure, and GCP regions seamlessly.
Data Exchange listings support controlled visibility, entitlement management, and auditing. Providers maintain full control over updates since consumers always access the live, authoritative version of the dataset.
Incorrect statements:
* It is not limited to internal sharing-external sharing is a major feature.
* ETL is not required because Snowflake's architecture exposes shared objects directly.

NEW QUESTION # 81
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