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Snowflake SOL-C01 Exam Syllabus Topics:

Section	Objectives
Data Loading & Unloading	- Data ingestion methods 1. Continuous data loading concepts 2. Bulk loading with stages
Account & Resource Management	- Snowflake account administration 1. Billing and usage tracking 2. Resource monitors
Data Transformation & Querying	- SQL and query processing 1. Query execution in Snowflake 2. Data transformation techniques
Performance & Optimization	- Performance tuning 1. Warehouse scaling strategies 2. Caching mechanisms
Snowflake Architecture & Data Platform	- Snowflake architecture concepts 1. Virtual warehouses 2. Separation of compute and storage
Security & Data Protection	- Access control and governance 1. Data encryption concepts 2. Role-based access control (RBAC)

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q146-Q151):

NEW QUESTION # 146
You are tasked with loading data from a Parquet file into a Snowflake table. The Parquet file contains a column with a timestamp datatype. However, the timestamp values are being incorrectly loaded into Snowflake due to a mismatch in the expected timestamp format. Which approach should you take to address this issue?

A. Create a view on top of the Parquet file and apply a 'TO_TIMESTAMP' function with the correct format.
B. Update the Snowflake table column datatype to TEXT and then CAST the values.
C. Set the 'TIME ZONE parameter at the session level before running the 'COPY INTO* command.
D. Modify the Parquet file to align with the default Snowflake timestamp format.
E. Use the 'TIMESTAMP_FORMAT option within the 'COPY INTO' command with the appropriate format string.

Answer: E

Explanation:
Option B is the most direct and efficient way to handle timestamp format discrepancies. By specifying the 'TIMESTAMP_FORMAT option within the 'COPY INTO' command, you can instruct Snowflake to parse the timestamp values in the Parquet file using the correct format string.
Option A is less practical as modifying the Parquet file might not always be feasible. Option C is also incorrect because Parquet files cannot be created on views. Option D addresses time zone issues, not format discrepancies. Option E will load them as text, which is not desirable.

NEW QUESTION # 147
A data engineer is tasked with loading JSON files containing customer reviews from an external stage into a Snowflake table. The JSON files have varying schemas and nested structures.
Which of the following methods is the MOST efficient and scalable way to ingest and query this data, minimizing the need for upfront schema definition?

A. Create a VARIANT column in a Snowflake table. Load the JSON data into the VARIANT column using a COPY INTO statement. Query the data using dot notation and FLATTEN function as needed.
B. Create an external table using the CREATE EXTERNAL TABLE command, pointing to the external stage.
Define a schema on the external table based on a representative JSON file. Query the data directly from the external table.
C. Use a stored procedure to parse the JSON files and insert the data into a relational table with a predefined schema. Implement error handling within the stored procedure to handle schema variations.
D. Load the JSON data into a cloud storage location, then use a Spark cluster to process and transform the data into a structured format before loading it into Snowflake.
E. Create a relational table with predefined columns based on the expected schema. Use a COPY INTO statement to load the data, handling schema variations through error handling in the COPY INTO statement.

Answer: A

Explanation:
Using a VARIANT column is the most efficient way to load JSON data with varying schemas because it allows you to load the data as is without defining a rigid schema upfront. Dot notation and the FLATTEN function allow you to query the data flexibly. Creating a relational table (Option A) requires defining a schema upfront which is not ideal for varying schemas. External tables (Option C) still require a schema definition. Stored procedures (Option D) can be complex and less scalable. Using Spark (Option E) adds unnecessary complexity and cost for this scenario.

NEW QUESTION # 148
What is a database in Snowflake?

A. A logical grouping of schemas.
B. A collection of tables and views.
C. A physical storage location for data files.
D. A single virtual warehouse.

Answer: A

Explanation:
A Snowflake database is alogical containergrouping one or more schemas. It does not store data physically in the traditional sense-Snowflake abstracts physical storage into its cloud-native micro-partitioned storage layer. Because schemas contain tables, views, and other objects, a database organizes schemas logically, enabling governance, naming conventions, and security boundaries.
A virtual warehouse is compute, not storage. A "collection of tables and views" better describes a schema, not a database. Snowflake separates logical structure from physical storage, so a database is not a physical file repository.
Thus, a database is best defined as a logical grouping of schemas.

NEW QUESTION # 149
When a virtual warehouse is scaled up (e.g., from X-Small to Small), what is the primary impact?

A. It can process larger and more complex queries faster.
B. It reduces the cost of running queries.
C. It gains more storage capacity.
D. It allows more concurrent users to connect.

Answer: A

Explanation:
Scaling up a warehouse increases compute resources (CPU, memory), improving performance on heavy workloads: large joins, transformations, and long-running analytical queries.
It does not increase storage, reduce cost, or guarantee higher concurrency (multi-cluster handles concurrency).

NEW QUESTION # 150
You are tasked with transferring ownership of a database named 'FINANCIAL DATA' from the
'ACCOUNTADMIN' role to a custom role named 'DATA GOVERNANCE'. After the transfer, the
'DATA GOVERNANCE' role should have full control over the database. Which of the following steps must be performed to ensure a successful ownership transfer?

A. Execute the command: 'GRANT OWNERSHIP ON DATABASE FINANCIAL DATA TO ROLE DATA GOVERNANCE;'
B. Grant the 'OWNERSHIP privilege on the database to the 'DATA_GOVERNANCE role using
'GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO _GOVERNANCE$. The user
executing this command must have the 'ACCOUNTADMIN' role, and the 'DATA_GOVERNANCE' role must already have ROLE DATA the 'USAGE privilege on the database and all schemas within it.
C. Grant the 'OWNERSHIP' privilege on the database to the 'DATA_GOVERNANCE role using
'GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO ROLE DATA_GOVERNANCE;'
and ensure the role is active. No additional privileges are required.
D. Grant the OWNERSHIP' privilege on the database to the 'DATA_GOVERNANCE role using
'GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO ROLE DATA GOVERNANCE$.
Then, explicitly grant all other required privileges (e.g., USAGE, CREATE) on the database to the
`DATA GOVERNANCE role.
E. Grant the 'OWNERSHIP' privilege on the database to the 'DATA_GOVERNANCE role using
"GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO ROLE DATA GOVERNANCE$.
The DATA GOVERNANCE role must be the current role for the user performing the operation.

Answer: C

Explanation:
The `GRANT OWNERSHIP command automatically revokes the OWNERSHIP privilege from the previous owner and grants it to the new owner. The user executing must have the necessary privileges to grant OWNERSHIP. The role receiving ownership needs no special privileges beforehand, as it is gaining complete control. Option C suggests 'USAGE privilege must be granted beforehand, which is not necessarily needed to transfer Ownership. Option D implies other privileges must be explictly granted but 'OWNERSHIP' already grants all permissions.
Option E requires that the DATA_GOVERNANCE role must be current role for user which is unnecessary.

NEW QUESTION # 151
......

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