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Updated Jan-2026 Pass SOL-C01 Exam - Real Practice Test Questions

NEW QUESTION # 84
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?

• A. 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.
• B. Create a clone of 'STG_CUSTOMERS' before reverting it to its previous state using Time Travel.
  Analyze the cloned table to identify the bug.
• C. Immediately drop and recreate the 'STG CUSTOMERS' table from a backup. Investigate the bug later by examining code logs.
• D. Set the 'DATA RETENTION_TIME IN DAYS parameter to 0 on 'STG_CUSTOMERS and perform a full table refresh from the source system.

Answer: B

Explanation:
Creating a clone of 'STG_CUSTOMERS' before reverting it allows you to preserve the incorrect data for analysis while restoring the original table to its correct state using Time Travel. This allows for both data recovery and root cause analysis. Option A doesn't preserve the incorrect data for analysis. Option C loses the incorrect data, hindering debugging. Option D disables Time Travel and relies on a full refresh, which might be slow and complex.

NEW QUESTION # 85
You are designing a data warehouse in Snowflake and need to implement a role-based access control (RBAC) system. You have the following requirements: A 'DATA ENGINEER role that can create and manage tables, views, and other objects in the 'RAW DATA' schema. A 'DATA ANALYST role that can query data from tables and views in the 'RAW DATA' schema and create temporary tables for their analysis. Neither role should be able to drop tables owned by the other role. Which set of SQL statements will correctly implement the desired RBAC system? (Select two that apply.)

• A.
• B.
• C.
• D.
• E.

Answer: A,B

Explanation:
Options A and B are correct because they grant the necessary privileges to the
'DATA_ENGINEER and "DATA_ANALYST roles. Option A uses FUTURE grants to the DATA ANALYST role; any new tables created will grant select permissions automatically. Option B explicitly grants SELECT on ALL TABLES, meaning it's a one-time grant. Option C grants ownership, which allows the to drop tables created by other roles. Option D grants ALL PRIVILEGES, which allows the 'DATA ENGINEER to drop tables created by other roles. Option E grants create temporary table on SCHEMA, it has to be on DATABASE.

NEW QUESTION # 86
What does the SELECT * statement do in a Snowflake query?

• A. Retrieves all columns from a specified table or view
• B. Retrieves only distinct values
• C. Retrieves only the primary key column
• D. Retrieves a limited number of rows

Answer: A

Explanation:
The SELECT * statement instructs Snowflake to returnall columnsfrom the referenced table or view. This is commonly used during data exploration, debugging, initial data profiling, and validation steps. It allows users to quickly view the complete dataset structure without manually specifying each column name.
However, while SELECT * retrieves all columns, it does not limit the number of rows. To restrict rows, developers must include aLIMITclause (e.g., SELECT * FROM table LIMIT 10;).
The query does not automatically apply DISTINCT or primary key filtering-Snowflake returns all rows exactly as stored unless additional filtering, WHERE conditions, or ordering are provided.
Though SELECT * is convenient, Snowflake best practices recommend explicitly selecting columns in production workloads to optimize performance and avoid unnecessary scanning of unused fields.

NEW QUESTION # 87
Which SQL command is used to view the definition of a Table?

• A. SHOW TABLE
• B. SELECT GET_DDL('TABLE','<table_name>');
• C. DESCRIBE TABLE
• D. SELECT * FROM INFORMATION_SCHEMA.ABLES

Answer: B

Explanation:
TheGET_DDLfunction retrieves the Data Definition Language (DDL) script that was used to create a given object. Executing:
SELECT GET_DDL('TABLE', 'MY_TABLE');
returns the full DDL including column definitions, comments, clustering keys, constraints, masking policies, and other metadata. This makes it essential for schema migration, replication, compliance audits, and environment synchronization.
DESCRIBE TABLEshows only column-level metadata-names, types, nullability-but does not return the full DDL.
SHOW TABLEis not a valid Snowflake command; tables are listed using SHOW TABLES.
INFORMATION_SCHEMA.TABLES lists table metadata, but the provided option "ABLES" is invalid and still would not provide DDL even if corrected.
Thus,GET_DDL()is the only method that returns the complete table definition directly.

NEW QUESTION # 88
A data provider wants to share a secure view 'SECURE VIEW SALES' from database 'DB RETAIL' to a consumer account using a private listing within the Snowflake Data Exchange. The provider has already created the listing. Which of the following steps are necessary to grant the consumer access, assuming the consumer account identifier is 'XYZ 12345'?

• A. Grant the 'IMPORTED PRIVILEGES' privilege on the share to the account `XYZ12345'.
• B. Add the consumer account 'XYZ12345 to the listing from the Data Exchange provider interface.
• C. Create a new role, grant 'SELECT on to the role, and grant that role to the consumer account
  'XYZ12345'.
• D. Grant 'SELECT' privilege on the share to the account 'XYZ12345.
• E. Grant the `IMPORTED PRIVILEGES privilege on the database created from the share to a role and then grant that role to the consumer account 'XYZI 2345.

Answer: B

Explanation:
Adding the consumer account to the private listing from the Data Exchange provider interface is the action that allows the consumer to access the share. The other options are not directly related to providing access via a private listing within the Data Exchange. Granting privileges on the share is typically done for direct shares, not data exchange shares. Consumers consume the whole shared database.

NEW QUESTION # 89
A Snowflake account has the following network policy configured: { "name": "corporate_network",
"allowed_ip_list": ["192.168.1.0/24", "10.0.0.10"], "blocked_ip_list": ["192.168.1.5", "10.0.0.0/16"] } Assuming this policy is active for the entire Snowflake account, which of the following IP addresses would be allowed to connect to Snowflake?

• A. 10.0.0.10
• B. 192.168.1.5
• C. 192.168.1.10
• D. 172.16.0.1
• E. 10.0.0.15

Answer: A,C

Explanation:
The network policy first allows IP addresses in the and then blocks IP addresses in the .
'192.168.1.10' falls within the '192.168.1.0/24' range and is not specifically blocked, so it's allowed. '10.0.0.10' is explicitly allowed in the '192.168.1.5 is explicitly blocked. '10.0.0.15' falls within the blocked range '10.0.0.0/16'. '172.16.0.1' is not in any of the allowed ranges.

NEW QUESTION # 90
A data engineering team is loading data from a CSV file containing customer information into a Snowflake table. The CSV file occasionally contains malformed data, such as missing fields or fields with incorrect data types (e.g., a string in a numeric field). The team wants to ensure that the loading process continues even when such errors occur, and that the problematic records are tracked for later analysis. Which of the following COPY INTO options should be used to achieve this, and how should the error be handled?

• A. Use 'ON ERROR = 'CONTINUE'` to allow the COPY statement to proceed even when errors are encountered, and use = FALSE' to handle varying column counts. Direct invalid records to a quarantine table using 'VALIDATION_MODE = RETURN ALL ERRORS.
• B. Use 'ON_ERROR = 'SKIP_FILE" along with the 'SIZE_LIMIT parameter to limit the size of the file that can be skipped.
• C. Use 'ON_ERROR = 'SKIP_FILE" to skip the entire file if any errors are encountered, and use
  'VALIDATION_MODE = to log all errors for later analysis.
• D. Use 'ON_ERROR = 'SKIP_FILE" to skip the entire file if any errors are encountered, without logging details of the error.
• E. Use 'ON ERROR = 'CONTINUE'` to allow the COPY statement to proceed even when errors are encountered, and use 'VALIDATION MODE = along with ` RESULT_SCAN' to retrieve error details and store them in a separate error table. Consider setting `ERROR MISMATCH = FALSE' depending on file structure variability.

Answer: E

Explanation:
'ON_ERROR = 'CONTINUE'` ensures that the load continues despite errors.
'VALIDATION_MODE = RETURN_ERRORS' allows you to retrieve details about the errors using the RESULT SCAN' function, which can then be stored in a separate error table. This allows for later analysis of problematic records. = FALSE is necessary if the column count varies between input records.

NEW QUESTION # 91
A Snowflake Notebook is configured to run a series of data processing steps on a schedule using the Snowflake Task feature. One of the steps involves calling an external API to enrich the data.
Due to network instability, the API calls occasionally fail, causing the entire task to fail. You want to implement error handling within the notebook to retry failed API calls up to a certain number of times before giving up. Assuming you are using Python within the Snowflake Notebook and Snowpark, how would you best implement this retry mechanism while ensuring minimal disruption to the notebook's workflow and preserving error information?

• A. Use a dedicated Python library like 'tenacity' within the Snowflake Notebook. Configure `tenacity' to retry the API call with exponential backoff and custom exception handling. Log all retry attempts and final error messages.
• B. Wrap the API call within a 'try...except' block. Inside the `except' block, use `time.sleep()' to introduce a delay, and then recursively call the function containing the API call until the maximum number of retries is reached. If all retries fail, log the error and re-raise the exception.
• C. Utilize the Snowflake Task's built-in error handling capabilities to automatically retry the task upon failure. Configure the task to retry a specific number of times with a specified delay between retries.
• D. Use the `snowflake.snowpark.functions.call_udf function with the 'retry_count' parameter set to the desired number of retries. This will automatically handle retries for IJDF calls within the notebook.
• E. Implement a custom decorator function that handles retries. The decorator should catch exceptions from the API call, log the error, introduce a delay, and retry the API call until the maximum number of attempts is reached. Apply this decorator to the function containing the API call.

Answer: A

Explanation:
Using a dedicated library like `tenacity' (E) provides the most robust and flexible retry mechanism.
It offers features like exponential backoff, customizable retry strategies, and detailed logging.
While `try...except' blocks (A) can work, they are less elegant and harder to maintain for complex retry logic. Snowflake does not have direct error handling mechanisms for external API calls (C, D) within notebooks. A decorator function (B) can work, but `tenacity' provides more features with less code.

NEW QUESTION # 92
Which statement is true regarding internal stages?

• A. Internal stages can only be used for structured data
• B. Internal stages can only be used for semi-structured data
• C. Internal stages are accessed with external cloud provider credentials
• D. Internal stages are managed and stored within Snowflake

Answer: D

Explanation:
Internal stages are fullySnowflake-managed storage locationsused to temporarily store data files for loading or unloading operations. Examples include:
* User stages (@~)
* Table stages (@%table_name)
* Named internal stages (@my_internal_stage)
Because they resideinside Snowflake, users do not need AWS, Azure, or GCP credentials to access them.
Internal stages support all file types Snowflake can process, including CSV, JSON, Parquet, Avro, ORC, images, PDFs, and other semi-structured formats.
Incorrect statements:
* They arenot limitedto structured or semi-structured data-they accept all supported formats.
* External cloud credentials are only needed for external stages (S3, Blob, GCS).
Internal stages simplify secure data ingestion and operational pipelines because Snowflake handles encryption, metadata, and lifecycle management.

NEW QUESTION # 93
How can you search for listings on Snowflake Marketplace? (Choose any 2 options)

• A. Contact Snowflake Support
• B. Browsing categories and tags
• C. Using the search bar at the top of the marketplace
• D. User Universal Search

Answer: B,C,D

Explanation:
Snowflake Marketplace provides multiple intuitive discovery mechanisms to help users explore available listings efficiently. Thesearch bar at the top of the Marketplaceis a primary entry point, enabling keyword- based discovery across providers, industries, and dataset types. This facilitates targeted searching for specific topics or data categories.
Universal Search, available in Snowsight, extends search beyond databases and worksheets to include Marketplace listings. This unified search experience allows users to locate datasets without navigating through menus, improving productivity and discoverability.
Browsing categories and tagssupports exploratory search by grouping listings under predefined themes such as financial data, geospatial data, demographics, and more. These tags promote structured navigation, especially useful when users are exploring rather than seeking something specific.
Contacting Snowflake Supportis not required for accessing or discovering listings and is irrelevant to Marketplace search workflows.

NEW QUESTION # 94
A data engineer is trying to create a new internal stage named in Snowflake using the following command: 'CREATE OR REPLACE STAGE FILE FORMAT = (TYPE = CSV COMPRESSION = GZlP)f. After running the command, they receive an error stating 'SQL compilation error: Object does not exist, or operation cannot be performed.'. What is the MOST likely reason for this error?

• A. The specified file format does not exist or is invalid.
• B. A stage with the same name already exists and the user does not have permissions to replace it.
• C. The user does not have the necessary privileges to create stages in Snowflake.
• D. No database or schema is currently selected for the session context.
• E. Internal stages cannot use the GZIP compression format.

Answer: D

Explanation:
The error 'Object does not exist, or operation cannot be performed.' typically occurs when the database and schema are not explicitly specified, and the session context is not set, so Snowflake doesn't know where to create the stage. While permission issues (Option A) can cause errors, the error message is typically different and specifies insufficient privileges. Options B and E are incorrect because CSV with GZIP is a valid file format for internal stages. Option C, while possible, is less likely because the 'CREATE OR REPLACE clause should handle replacing the stage if the user has sufficient permissions.

NEW QUESTION # 95
A Snowflake environment has two databases, 'DEV DB' and 'PROD DB'. A table 'EMPLOYEES' exists in both databases with identical schemas. A developer needs to create a view in 'DEV DB' that references the 'EMPLOYEES' table in 'PROD DB' to perform cross-database joins. Which of the following SQL statements will successfully create this view in 'DEV DB'?

• A. CREATE OR REPLACE VIEW DEV DB.PUBLIC.EMPLOYEE VIEW AS SELECT FROM PROD DB.PUBLIC.EMPLOYEES;
• B. CREATE VIEW DEV DB.PUBLIC.EMPLOYEE VIEW AS SELECT FROM PROD
  DB.PUBLIC.EMPLOYEES WITH READ ONLY;
• C. CREATE SECURE VIEW DEV DB.PUBLIC.EMPLOYEE VIEW AS SELECT FROM PRODDB.PUBLIC.EMPLOYEES;
• D. CREATE OR REPLACE VIEW DEV DB.PUBLIC.EMPLOYEE VIEW AS SELECT FROM PROD DB.PUBLIC.EMPLOYEES WITH SECURE;
• E. CREATE VIEW DEV DB.PUBLIC.EMPLOYEE VIEW AS SELECT FROM PROD
  DB.PUBLIC.EMPLOYEES;

Answer: A,C

Explanation:
To create a view that references objects in other databases, you should fully qualify the object names in the 'SELECT statement. Both options create the view referencing the PROD DB. Option C uses `CREATE OR REPLACE VIEW and option E uses `CREATE SECURE VIEW' which are correct and commonly used options for this scenerio. 'WITH READ ONLY' is not a supported clause for creating views in Snowflake. 'CREATE SECURE VIEW' creates a secure view which is recommended to protect the underlying objects' definition.

NEW QUESTION # 96
What are the three key layers of Snowflake's architecture?

• A. Data Storage, Query Processing, Cloud Services
• B. Data Repository, Compute Clusters, Metadata Management
• C. Authentication, Access Control, Cloud Services
• D. Database Storage, ODBC Drivers, Virtual Warehouses

Answer: A

Explanation:
Snowflake's architecture is built on three primary layers that separate and optimize storage, compute, and services. TheData Storagelayer stores all structured and semi-structured data in Snowflake's optimized, compressed columnar format. Snowflake manages file organization, metadata, and optimizations automatically, ensuring efficient storage management. TheQuery Processinglayer is implemented through virtual warehouses that independently execute queries using MPP (massively parallel processing) compute clusters. These warehouses scale elastically based on workload requirements. TheCloud Serviceslayer manages metadata, authentication, access control, query optimization, and infrastructure orchestration. This separation allows independent scaling of compute and storage, optimizing performance while simplifying administration.

NEW QUESTION # 97
A Snowflake table 'ORDERS' contains a 'ORDER DATE' column of data type DATE. You need to write a query that returns the order count for each month in the year 2023. Which of the following SQL snippets is MOST efficient and accurate for achieving this?

• A. Option D
• B. Option B
• C. Option A
• D. Option C
• E. Option E

Answer: D

Explanation:
DATE TRIJNC is the most efficient way to truncate dates to a specified level (month in this case) and allows for accurate grouping (C). It also takes advantage of Snowflake's date functions optimized for performance. Using MONTH and YEAR (A, B) are functional, but less performant than DATE TRUNC. While D works, converting to a CHAR and then grouping is less efficient. E retrieves the month name, which wasn't the requirement, and month names may not group correctly due to localization settings. Using BETWEEN on the DATE column is also more efficient than extracting year and comparing as it leverages indexing when available.

NEW QUESTION # 98
Which of the following is a potential use case for the CLASSIFY_TEXT function?

• A. Generating SQL code from natural language description
• B. Translating a user manual into multiple languages
• C. Categorizing customer support tickets based on topic
• D. Extracting data from scanned invoices

Answer: C

Explanation:
CLASSIFY_TEXTis a Snowflake Cortex task-specific function designed to categorize free-form text into predefined buckets. It operates by applying LLM-driven classification logic based on category labels or descriptions provided by the user. A primary and practical use case isautomatically categorizing customer support ticketsbased on content, such as identifying billing issues, login problems, or product defects. This enables automated routing, prioritization, and operational analytics.
The function outputs structured results with the predicted label and confidence score. It is optimized for domain classification rather than open-ended text generation.
Incorrect options:
* Generating SQL codeis handled by the COMPLETE function.
* Translationis handled by TRANSLATE.
* Extracting structured fields from documentsis the role of PARSE_DOCUMENT.
Thus, classification of incoming text streams is the natural use case of CLASSIFY_TEXT.

NEW QUESTION # 99
Which cloud platforms does Snowflake support?

• A. All of the options
• B. Microsoft Azure
• C. Google Cloud Platform (GCP)
• D. Amazon Web Services (AWS)

Answer: A

Explanation:
Snowflake is a fully cloud-native platform that supports deployment across all three major cloud providers:
AWS, GCP, and Azure. Snowflake delivers consistent functionality across each provider, allowing organizations to choose the cloud best suited for regulatory, architectural, and business requirements.
Although small feature differences may exist between cloud providers, Snowflake's core capabilities-virtual warehouses, storage layers, security, data sharing, and governance-operate uniformly across all three platforms. This multi-cloud support gives Snowflake strong flexibility for hybrid, multi-region, and multi- cloud deployments.

NEW QUESTION # 100
You are tasked with creating a secure data sharing environment in Snowflake. You need to share a table named 'CUSTOMER RECORDS' from the `SALES DB' database in your account (Account A) with another Snowflake account (Account B). You want to ensure that Account B can only query the data and cannot modify it in any way. Which of the following steps are REQUIRED to achieve this securely and effectively?

• A. Create a share object in Account A and grant USAGE privilege on the database and SELECT privilege on the table to the share.
• B. Grant OWNERSHIP on the `CUSTOMER RECORDS table to the target account (Account B).
• C. Create a database in Account B from the share provided by Account A, and grant SELECT privilege to the roles in Account B that need access.
• D. Create a masking policy on all the columns of the 'CUSTOMER RECORDS table before creating the share object.
• E. Create an external function that reads the data from Account A and presents it to Account B.

Answer: A,C

Explanation:
To share data securely, you need to create a share object and grant appropriate privileges in the source account (A). This includes USAGE on the database and SELECT on the table. The target account (B) then creates a database from the share, which is a read-only copy of the data.
Finally, SELECT privilege must be granted to roles within account B to allow access. Granting OWNERSHIP to the target account (C) defeats the purpose of secure sharing and isn't required or best practice. External functions (D) are not the correct approach. Masking Policies (E) might be appropriate based on data sensitivity but aren't a fundamental requirement for simple data sharing.

NEW QUESTION # 101
What types of worksheets can be created in Snowsight? (Select TWO).

• A. Scala
• B. Javascript
• C. SQL
• D. Java
• E. Python

Answer: C,E

Explanation:
Snowsight supports two worksheet types:SQL worksheetsandPython worksheets. SQL worksheets allow users to execute queries, create objects, and perform data analysis using ANSI SQL and Snowflake-specific extensions. Python worksheets, powered by Snowpark, allow users to write Python code that interacts directly with Snowflake tables, data frames, and machine learning workflows.
Java, Scala, and JavaScript are supported via Snowpark APIs or UDF development, but they cannot be used as worksheet languages. Worksheets are designed for interactive analysis, visualization, and iterative development, with native runtimes only for SQL and Python.
Thus, only SQL and Python worksheets can be created within Snowsight.

NEW QUESTION # 102
What is the primary purpose of the CLASSIFY_TEXT function in Cortex?

• A. Classifying text into predefined categories
• B. Translating text to different languages
• C. Extracting data from documents
• D. Generating text completions based on a given prompt

Answer: A

Explanation:
TheCLASSIFY_TEXTfunction performscategory assignmentfor free-form text. Users provide a list of predefined labels, and the function predicts the most appropriate category using a Snowflake-optimized LLM.
It is ideal for automatically tagging customer feedback, routing support tickets, labeling product reviews, classifying email content, and segmenting operational logs.
The function outputs both the predicted label and an associated confidence score, enabling downstream filtering, dashboarding, and automated processing.
Other options belong to different Cortex functions:
* TRANSLATEhandles multilingual translation.
* COMPLETEgenerates natural language completions.
* PARSE_DOCUMENTextracts structured information from documents.
Thus, CLASSIFY_TEXT is task-specific and optimized specifically for classification workloads.

NEW QUESTION # 103
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