Updated Nov-2025 100% Cover Real SOL-C01 Exam Questions – 100% Pass Guarantee [Q132-Q151]

NO.132 A data analyst needs to create a view named that aggregates sales data by month. The base table sales_transactions’ contains columns ‘transaction_id’ , ‘transaction_date’ , and
‘sales_amount’ . The analyst wants to ensure that only users with the ‘ANALYST’ role can query this view. Which of the following SQL statements correctly creates the view and grants the necessary privileges?

Option A

Option B

Option C

Option D

Option E

NO.133 A Snowflake administrator is tasked with managing storage costs. They notice that several large tables are rarely queried. Which of the following strategies are MOST effective for reducing storage costs for these tables, while still allowing for future access?

Dropping the tables and recreating them when needed from source data.

Cloning the tables to a cheaper storage solution outside of Snowflake.

Setting a data retention period for the tables using Time Travel and Fail-safe.

Purging the tables completely to reclaim storage space.

Using Snowflake’s data compression, which is automatically enabled and optimizes storage without any manual intervention.

NO.134 You need to transfer ownership of a table named ‘CUSTOMER DATA’ in the ‘SALES’ schema of the ‘RETAIL DB’ database from the ‘ANALYST ROLE’ to the ‘DATA ENGINEER ROLE’. However,
‘DATA ENGINEER ROLE already has the ‘SELECT’ privilege on the table. You want to achieve this transfer with minimal downtime and ensure that the ‘DATA ENGINEER ROLE retains all existing privileges after the ownership transfer. Which command is the most efficient and correct way to accomplish this?

NO.135 A Snowflake administrator notices that a virtual warehouse named ‘REPORTING WH’ is frequently idle during off-peak hours. They want to optimize costs by ensuring the warehouse suspends automatically when not in use, but they also need to minimize the latency for the first query after a period of inactivity. Which combination of actions would achieve this goal?

Set ‘AUTO SUSPEND to a low value (e.g., 60 seconds) and set ‘AUTO RESUME to FALSE.

Set ‘AUTO_SUSPEND’ to a low value (e.g., 60 seconds) and ensure AUTO_RESUME is TRUE.

Set ‘AUTO SUSPEND to a high value (e.g., 3600 seconds) and set `AUTO RESUME to TRUE.
Also, Enable Result Cache.

Set ‘AUTO SUSPEND to NULL and set ‘AUTO RESUME to FALSE.

Set ‘AUTO SUSPEND to a low value (e.g., 60 seconds), ensure `AUTO RESUME is TRUE, and ensure commonly used tables and data are clustered correctly to minimize initial query scan time.

NO.136 A data engineer executed several queries in Snowflake using Snowsight, and now needs to identify the specific queries that inserted data into the ‘SALES TABLE’ table within the ‘SALES DB’ database and ‘PUBLIC’ schema. Which Snowsight feature or filtering option within the Query History interface is MOST effective for achieving this?

Filtering the Query History by the ‘User’ who executed the queries.

Filtering the Query History by the ‘Warehouse’ used during query execution.

using the ‘Text’ filter in Query History with the search string ‘INSERT INTO

Filtering the Query History by ‘Status’ to only show ‘Successful’ queries.

Filtering the Query History using the ‘Query ID’ field to narrow down the list.

NO.137 You have created a new database named ‘DEV DB’ and want to grant the “SELECT’ privilege on all tables within this database to the ‘ANALYST’ role. You want to ensure that any new tables created in ‘DEV DB’ in the future automatically grant ‘SELECT’ to the ‘ANALYST’ role. Which of the following approaches achieves this?

GRANT SELECT ON ALL TABLES IN DATABASE DEV DB TO ROLE ANALYST;

GRANT FUTURE GRANTS SELECT ON DATABASE DEV DB TO ROLE ANALYST;

GRANT SELECT ON FUTURE TABLES IN DATABASE DEV DB TO ROLE ANALYST;

GRANT SELECT ON DATABASE DEV DB TO ROLE ANALYST; ALTER DATABASE DEV DB SET ENABLE FUTURE GRANTS = TRUE;

ALTER DATABASE DEV DB SET GRANTED PRIVILEGES = ‘SELECT’ TO ROLE ANALYST;

NO.138 You have a Snowflake external stage configured to access Parquet files in an AWS S3 bucket.
You want to query these Parquet files directly using Snowflake without explicitly loading them into a table. You also want to enable directory tables for this external stage. Which of the following steps are necessary to enable directory tables for the external stage and query the data?

Create an external stage pointing to the S3 bucket, then execute ‘ALTER STAGE SET ENABLE_DIRECTORY = TRUE;’

Create an external stage pointing to the S3 bucket, then execute ‘ALTER STAGE REFRESH;’ , then execute ‘ALTER STAGE SET ENABLE_DIRECTORY =TRUE;’

Create an external stage pointing to the S3 bucket, execute ‘ALTER STAGE SET ENABLE_DIRECTORY = TRUE;’ , then refresh the directory table using ‘ALTER STAGE REFRESH;’.

Create an external stage pointing to the S3 bucket with ‘ENABLE DIRECTORY = TRUE specified during stage creation, then refresh the directory table using ‘ALTER STAGE REFRESH;’.

Create an external stage pointing to the S3 bucket with ‘ENABLE DIRECTORY = TRUE specified during stage creation, then no refresh is needed. Directory tables are automatically updated.

NO.139 A junior data engineer is using the Snowflake web UI to create a view based on a complex query against a large dataset. They want to optimize the view for performance. Which of the following considerations and actions, when implemented directly through the Snowflake web UI and related SQL commands, will most effectively improve the performance of queries against this view?

Create the view as a materialized view instead of a regular view, using the ‘CREATE MATERIALIZED VIEW’ command in a worksheet.

Ensure that the underlying tables used by the view have appropriate clustering keys defined and maintained.

Use the `WITH SECURE option when creating the view to prevent unauthorized access, as this automatically optimizes query performance.

Manually rewrite the query within the view definition to utilize specific table aliases and join orders that are known to be faster based on previous query profiling.

Partition the underlying tables based on a relevant date column before creating the view.

NO.140 You need to load a large CSV file (1 TB) into a Snowflake table. Which of the following methods would generally provide the fastest loading performance?

Using the Snowflake web interface to upload the file.

Using a Python script with the Snowflake Connector to insert rows individually.

Using Snowpipe with a properly sized warehouse and a file format object optimized for CSV files.

Using the ‘COPY INTO’ command with multiple parallel streams and a properly sized warehouse and a file format object optimized for CSV files.

Using the ‘INSERT INTO statement directly within a Snowflake worksheet.

NO.141 You are designing a data pipeline in Snowflake that involves frequent updates to a staging table
`STG CUSTOMERS before merging the data into a production table `PROD CUSTOMERS. The
‘DATA RETENTION_TIME parameter is set to 7 days at the account level. During a particular data load, a bug in the pipeline causes incorrect data to be loaded into `STG CUSTOMERS. You need to revert ‘STG CUSTOMERS’ to its state before the erroneous load. However, you also need to investigate the cause of the bug using the incorrect data in the current version of ‘STG CUSTOMERS. What steps can you take to achieve both data recovery and root cause analysis effectively?

Immediately drop and recreate the ‘STG CUSTOMERS’ table from a backup. Investigate the bug later by examining code logs.

Create a clone of ‘STG_CUSTOMERS’ before reverting it to its previous state using Time Travel.
Analyze the cloned table to identify the bug.

Revert ‘STG CUSTOMERS to its previous state using Time Travel. The incorrect data is unrecoverable as Time Travel replaces the current data. The bug will have to be found later.

Set the ‘DATA RETENTION_TIME IN DAYS parameter to 0 on ‘STG_CUSTOMERS and perform a full table refresh from the source system.

NO.142 You have been tasked with configuring a session parameter for all users connecting to a specific Snowflake database named REPORTING DB’. You want to set the ‘TIMEZONE parameter to
‘America/Los_Angeles’ for all sessions within this database. Which of the following SQL statements would BEST accomplish this?

ALTER SESSION SET TIMEZONE = ‘America/Los_Angeles’;

ALTER ACCOUNT SET TIMEZONE = ‘America/Los_Angeles’;

ALTER USER SET TIMEZONE = ‘America/Los_Angeles’;

ALTER DATABASE REPORTING_DB SET TIMEZONE = ‘America/Los_Angeles’;

ALTER WAREHOUSE SET TIMEZONE = ‘America/Los_Angeles’;

NO.143 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?

Modify the Parquet file to align with the default Snowflake timestamp format.

Use the ‘TIMESTAMP_FORMAT option within the ‘COPY INTO’ command with the appropriate format string.

Create a view on top of the Parquet file and apply a ‘TO_TIMESTAMP’ function with the correct format.

Set the ‘TIME ZONE parameter at the session level before running the ‘COPY INTO* command.

Update the Snowflake table column datatype to TEXT and then CAST the values.

NO.144 A data engineer needs to transfer ownership of a database named ‘SALES DATA’ to a role named ‘DATA GOVERNANCE The engineer attempts to use the following command: ‘GRANT OWNERSHIP ON DATABASE SALES DATA TO ROLE DATA GOVERNANCE. However, this command fails. What is the MOST likely reason for the failure?

The `DATA GOVERNANCE role does not have the ‘OWNERSHIP’ privilege on the ‘SALES DATA’ database.

The correct syntax is ‘ALTER DATABASE SALES_DATA OWNER TO ROLE
DATA_GOVERNANCE.

The role executing the command does not have the ‘OWNERSHIP’ privilege on the `SALES DATA database.

Ownership can only be transferred to users, not roles.

The `SALES DATA database contains shared data; ownership cannot be transferred in such cases.

NO.145 Role ‘DATA ENGINEER’ owns the external stage. The security team mandates that ownership be transferred to ADMIN’. Post-transfer, which privileges are automatically revoked or changed for the ‘DATA ENGINEER role?

Option A

Option B

Option C

Option D

Option E

NO.146 You have a Snowflake table named ’employees’ with columns ’employee_id’, ‘name’,
‘department’, and ‘salary’. You need to load data from a fixed-width file located in an internal stage named ’employee_stage’. The file format is as follows: ’employee_id’ (5 characters), name’ (20 characters), ‘department’ (15 characters), and ‘salary’ (10 characters). Which of the following demonstrates the correct way to create the file format and then load the data into the ’employees’ table, assuming all columns are of type STRING in the target table?

Option A

Option B

Option C

Option D

Option E

NO.147 You’re writing a Snowflake Notebook that needs to dynamically create tables based on a list of table names stored in a Python list called ‘table_names’. You want to iterate through this list and execute a CREATE TABLE statement for each table name. However, the names might include spaces and special characters. Which approach or approaches can you use to achieve this goal safely and effectively?

Option A

Option B

Option C

Option D

Option E

NO.148 Consider the following Snowflake warehouse definition: ‘CREATE OR REPLACE WAREHOUSE MY WAREHOUSE WITH WAREHOUSE SIZE = ‘MEDIUM’ WAREHOUSE TYPE = ‘STANDARD’ AUTO SUSPEND = 300 AUTO RESUME = TRUE MIN CLUSTER_COUNT = 1 MAX CLUSTER_COUNT = 3 SCALING_POLICY = ‘ECONOMY;’ Which of the following statements are TRUE regarding this warehouse? (Select all that apply)

The warehouse will automatically suspend after 5 minutes of inactivity.

The warehouse will always have at least one active cluster.

The warehouse can scale up to a maximum of three clusters to handle concurrent queries.

The warehouse is optimized for minimizing costs rather than maximizing performance when scaling.

The ‘WAREHOUSE_TYPE = ‘STANDARD” setting is invalid and will cause an error.

NO.149 You are working with a Snowflake external stage that contains a mix of structured and unstructured data. Specifically, you have a directory in your S3 bucket that contains both CSV files and JSON files. You have enabled directory tables for this stage. You want to create a view that combines data from both the CSV and JSON files. How can you effectively achieve this using the directory table, considering that the CSV and JSON files have different schemas?

Create two separate views, one for the CSV files and one for the JSON files, using the
‘METADATA$FILENAME column in the directory table to filter the files for each view. Then, create a UNION ALL view on top of those views.

Create a single view that uses the ‘METADATA$FILENAME column in the directory table to dynamically parse the data based on the file extension (CSV or JSON) using conditional functions within the view definition.

Create a single external table that automatically detects the file format and schema based on the file extension and loads the data accordingly. Directory tables will then work automatically with this table.

It’s not possible to combine data from different file formats (CSV and JSON) directly using directory tables. You need to load the data into separate internal Snowflake tables first.

Create a stored procedure that reads the directory table metadata, determines the file type for each file, and then dynamically creates and executes SQL statements to load the data into a single table.

NO.150 Which of the following statements are correct regarding Snowflake Data Exchange and Data Marketplace? (Select TWO)

Data Exchange allows organizations to privately share data with specific partners, while Data Marketplace is a public forum for sharing data.

Both Data Exchange and Data Marketplace require data providers to create listings, but Data Exchange listings are only visible to invited consumers.

Data Marketplace supports both free and paid data listings, while Data Exchange only supports free listings.

Data Exchange enables the data provider to monitor which consumers are accessing their data and track usage, but Data Marketplace does not.

Data Exchange requires pre-negotiated contracts between providers and consumers, while Data Marketplace allows consumers to access data without direct provider approval.

NO.151 You are working with a Snowflake Notebook and need to execute a series of SQL statements that include both DDL (Data Definition Language) and DML (Data Manipulation Language) operations.
You want to ensure that if any statement fails, the entire sequence is rolled back. How can you achieve this within a single Notebook cell?

Wrap the SQL statements within a ‘BEGIN’ and ‘END’ block within the cell. Snowflake Notebooks automatically treat this as a transaction.

Use the ‘snowflake.connector’ Python library to explicitly manage transactions using , followed by individual SQL statements, and or based on success or failure.

Prefix each SQL statement with ‘TRY and include a ‘CATCH’ block at the end to handle any exceptions and perform a rollback if needed.

Snowflake Notebooks do not support transactions within a single cell. Each SQL statement is executed independently.

Create a stored procedure that encapsulates all SQL statements and then call the stored procedure from the notebook cell. The stored procedure can handle the transaction management.

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