Get 2025 Free Snowflake SOL-C01 Exam Practice Materials Collection [Q111-Q135]

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NEW QUESTION # 111
You have a table 'SALES DATA' with columns 'PRODUCT_ID' ONT), 'SALE_DATE' (DATE), and
'SALE AMOUNT' You want to load data into this table from various sources. You need to insert multiple rows in a single INSERT statement for performance reasons. However, one of the data sources occasionally provides 'SALE AMOUNT' as 'NULL'. What is the best way to handle the
'NULL' values in the 'INSERT' statement while ensuring that the rest of the data is loaded correctly?

A. Replace the `NULL ` values with 0 in the 'INSERT statement.
B. Omit the 'SALE AMOUNT column from the 'INSERT statement when the value is 'NULL'.
C. Use the 'DEFAULT keyword for the SALE AMOUNT column in the 'INSERT statement when the value is
D. Insert `NIJLC directly into the 'SALE_AMOUNT column.
E. Use in the insert statement to convert empty string to NULL.

Answer: D

Explanation:
Option E is the most straightforward and correct approach. If the 'SALE_AMOIJNT column is defined to allow 'NULL' values (which is common for numeric columns representing amounts), you can directly insert into the column. Option A will cause an error if all columns are not specified in INSERT statement. Option B changes the meaning of your data by changing the NULL to 0. Option C will only work if the 'SALE AMOUNT column has a default defined, and D is not needed as NULL can be inserted as NULL into the column.

NEW QUESTION # 112
A data engineer is tasked with designing a Snowflake solution for a financial services company that needs to perform real-time fraud detection. The solution needs to leverage external models trained in Python using Snowpark ML and ingest data from various sources, including Kafka streams and S3 buckets. Which of the following architectural choices would best leverage the Snowflake A1 Data Cloud capabilities for this scenario?

A. Utilize Snowflake's Snowpark ML to deploy and manage models directly within Snowflake, integrate with Kafka using Snowflake Kafka connector, and use external tables for S3 data access.
B. Use Snowflake's Streamlit integration for real-time data visualization of Kafka streams, and use Snowpark ML to deploy and manage models directly within Snowflake, loading data from S3 via COPY INTO.
C. Use Snowflake external functions to call a separate SageMaker endpoint for model inference and load data from Kafka using Snowpipe and S3 using COPY INTO.
D. Create a separate Spark cluster to process Kafka streams and use the Snowflake Spark connector to write the processed data to Snowflake. Use COPY INTO for S3 data, and deploy the ML model using Snowflake external functions calling a Databricks cluster.
E. Employ Snowflake's Snowpipe for Kafka integration, use Snowflake Tasks to schedule data loading from S3, and leverage Snowflake's native SQL capabilities for fraud detection without employing external models.

Answer: A,B

Explanation:
Option B and E offer the best solutions. Option B leverages Snowflake's Snowpark ML for model management within Snowflake, Snowflake Kafka connector for direct Kafka integration, and external tables to avoid data duplication for S3. Option E complements B by adding Streamlit integration for Real Time Visualization of Kafka Streams. Option A, C and D introduce unnecessary external dependencies (SageMaker, Spark, Databricks) and don't fully utilize Snowflake's native capabilities for data processing and ML model deployment. Choosing native capabilities minimizes complexity and operational overhead.

NEW QUESTION # 113
You are working with a Snowflake Notebook to process data from an external stage (AWS S3).
You need to access the S3 stage using a named stage object and a storage integration configured with IAM roles. Which of the following options represents the correct sequence of steps and Snowflake SQL commands within the notebook to achieve this?

A. 1. Create the storage integration with appropriate IAM roles. 2. Create the external stage referencing the storage integration. 3. Create a file format object. 4. Use `COPY INTO command to load data from the stage into a Snowflake table, specifying the stage name and the file format.
B. 1. Create the external stage specifying the S3 bucket URL and credentials. 2. Create a file format object. 3. Use 'SELECT FROM @stage/file.csv' to query the data directly from the stage.
C. 1. Create the storage integration with appropriate IAM roles. 2. Use the 'LIST @stage' command to verify the stage connectivity and file listing. 3. Create an external table pointing to the external stage. 4. Use the 'REFRESH EXTERNAL TABLE command to load the metadata. 5. Query data directly from the external table.
D. 1. Create the storage integration with appropriate IAM roles. 2. Create the external stage referencing the storage integration. 3. Use 'COPY INTO' command to load data from the stage into a Snowflake table, specifying the stage name.
E. 1. Create the external stage specifying the S3 bucket URL and credentials. 2. Create a file format object. 3. Use `COPY INTO' command to load data from the stage into a Snowflake table, specifying the stage name and the file format.

Answer: A,D

Explanation:
Options A and D are correct. The correct and secure approach involves using storage integrations with IAM roles. Creating the storage integration first establishes trust between Snowflake and AWS. The stage then references this integration. The 'COPY INTO' command (Option A) is used for loading data into Snowflake tables. External tables (option C) are read-only and designed for querying data in place, not loading it. Option D Corrects A by including the file format, which is generally required. Option B does not use storage integrations, which is more secure . Option E Requires credential in stage definition, which is not encouraged.

NEW QUESTION # 114
A data analyst is tasked with translating customer reviews from various languages into English to perform sentiment analysis. The customer reviews are stored in a table named 'CUSTOMER REVIEWS' with columns 'REVIEW ID', 'REVIEW TEXT', and 'LANGUAGE CODE'. However, the
'LANGUAGE CODE column is sometimes missing or inaccurate. What is the BEST approach to use the Snowflake Cortex LLM's 'TRANSLATE function to handle reviews where the language is unknown?

A. Use the 'DETECT LANGUAGE function from Snowflake Cortex LLM to automatically identify the source language before passing the review text and detected language code to the 'TRANSLATE function.
B. Use a CASE statement to check if 'LANGUAGE_CODE is null. If it is, pass the review text directly to 'TRANSLATE without specifying the source language, relying on automatic language detection by the function. If not null, use the LANGUAGE_CODE'.
C. Create a separate table containing default language codes for each region and join it with the
`CUSTOMER REVIEWS' table to fill in missing language codes before calling 'TRANSLATE.
D. Use a TRY_TRANSLATE function which does not exist, which would catch exception if LANGUAGE_CODE is invalid or Null.
E. Exclude all reviews with a missing 'LANGUAGE CODE from the translation process to avoid errors.

Answer: A

Explanation:
Option C is the best approach. Snowflake Cortex LLM provides a 'DETECT_LANGUAGE function that can automatically identify the language of the text. This allows you to handle cases where the UNGUAGE CODE is missing or inaccurate. Option A may not work as expected if automatic language detection is not reliable or supported directly within the TRANSLATE function. Option B introduces complexity and potential inaccuracies with default language codes. Option D avoids the problem but sacrifices valuable data.

NEW QUESTION # 115
You are using Snowflake notebooks to build a data pipeline. You want to parameterize a SQL query using Python variables to filter data based on a date range provided as input to the notebook. You have and 'end_date' Python variables. Which of the following is the most secure and efficient way to incorporate these variables into a SQL query executed within a Snowflake notebook using Snowpark?

Answer: A

Explanation:
Option B is the most secure and efficient way. Snowpark's 'session.sql()' method supports parameterized queries using ':1 , `:2' , etc., to represent placeholders for the Python variables.
This prevents SQL injection vulnerabilities and allows Snowflake to optimize the query execution plan by reusing it with different parameter values. Option A is vulnerable to SQL injection. Option C is a less secure string formatting method. Option D is prone to errors and SQL injection. Option E is not a valid way to parameterize the query in Snowflake with Snowpark API.

NEW QUESTION # 116
Which of the following types of data can be found on Snowflake Marketplace? (Choose any 3 options)

A. Proprietary data sets
B. Third-party data sets
C. Public data sets
D. Financial market data

Answer: B,C,D

Explanation:
Snowflake Marketplace serves as a centralized catalog where providers publishready-to-use datasets, models, and applicationsacross industries. It includes:
Public datasets, such as demographics, weather, healthcare statistics, and government data.
Financial market datasets, including equities, commodities, macroeconomic indicators, and pricing feeds from major financial data vendors.
Third-party datasetssupplied by external organizations specializing in geospatial intelligence, retail analytics, marketing insights, healthcare, census data, and more.
"Proprietary datasets" are internal to organizations and typically not published on the public Marketplace unless the owner chooses to list them. Marketplace listings are curated and governed.

NEW QUESTION # 117
What task can be performed on the Snowsight Schema Details page?

A. Change the schema name.
B. Truncate the schema data.
C. Edit the schema metrics.
D. Share a schema with a different account.

Answer: A

Explanation:
On the Snowsight Schema Details page, one of the supported operations is renaming the schema. The UI exposes controls that allow users with appropriate privileges to change the schema name, which can help maintain consistent naming conventions or reflect project reorganizations.
Sharing data with other accounts is typically done using secure shares at the database or object level, not from a simple "Share this schema" function on the Schema Details page. Truncation is a table-level operation (e.g., TRUNCATE TABLE), not something that applies at the schema level. Metrics visible on the Schema Details page (such as object counts or storage usage) are informational and not directly editable; they are derived from system metadata.

NEW QUESTION # 118
A data engineer is building a Snowflake Notebook to perform some data transformations. The notebook interacts with a Snowflake table named 'SALES DATA' which contains columns like
'ORDER ID', 'PRODUCT ID, 'SALE DATE, and `SALE AMOUNT. The engineer wants to use a Streamlit application to visualize the daily sales trends based on the transformed data. However, they are facing challenges with authenticating the Streamlit application to Snowflake and efficiently retrieving the data. Which of the following approaches would provide the most secure and efficient way to authenticate Streamlit to Snowflake and load the necessary data?

A. Creating a Snowflake user with extremely broad privileges and storing those credentials as Streamlit secrets.
B. Using a Snowflake access token generated outside of Streamlit and passing it as a Streamlit secret or environment variable. Leverage Snowflake Connector for Python in Streamlit to authenticate.
C. Using Snowflake's OAuth integration and leveraging Streamlit's ability to handle authentication flows to securely obtain and use temporary credentials.
D. Granting public access to the 'SALES_DATA' table and querying it directly from Streamlit without authentication.
E. Hardcoding Snowflake credentials (username and password) directly within the Streamlit application script.

Answer: B,C

Explanation:
Option B is correct because it uses a more secure approach than hardcoding credentials by leveraging Streamlit secrets for access token management. Option E is also correct as Snowflake OAuth integration allows leveraging standard authentication flows for secure credential handling within the streamlit application. Hardcoding credentials (A), creating overly permissive users (C) and granting public access (D) are all significant security risks. Using temporary credentials is the most secure best practice.

NEW QUESTION # 119
What is the default Time Travel retention period for standard tables in Snowflake?

A. 1 day
B. 0 days
C. 7 days
D. 14 days

Answer: A

Explanation:
Standard permanent tables in Snowflake have a default1-day Time Travel retention period. During this period, users may query historical data, restore dropped tables, or clone past states. Higher editions allow extending this period up to 90 days. Temporary and transient tables have 0-day retention by default.

NEW QUESTION # 120
Which of the following are benefits of using a multi-clustered warehouse in Snowflake? (Choose any 3 options)

A. Automatic scaling of clusters based on query demand
B. Reduced storage costs
C. Increased compute capacity
D. Improved query performance during peak load

Answer: A,C,D

Explanation:
A multi-cluster warehouse raises Snowflake's capability to handle concurrent workloads efficiently. When large numbers of users or queries hit the system, Snowflake can automatically start additional clusters (scaling out) to distribute workloads, reducing queuing and improving performance. As query demand drops, Snowflake scales in by shutting down clusters to conserve credits. This dynamic auto-scaling provides significantly increased compute capacity during peak usage periods and enhances responsiveness.
Importantly, multi-cluster warehouses affect only compute, not storage, so they do not reduce storage costs.
Their primary benefits revolve around performance stability, concurrency handling, workload isolation, and compute elasticity.

NEW QUESTION # 121
Which feature does Snowsight provide for query management?

A. Manual data storage configuration
B. Pre-built queries only
C. Limited to simple queries
D. An intuitive SQL editor for writing and running queries

Answer: D

Explanation:
Snowsight includes a modern, fully capableSQL editordesigned for rich query development, execution, and analysis. The editor offers features such as syntax highlighting, intelligent auto-complete, contextual error display, and result-set visualization. Users can organize work with worksheets, folders, tagging, and versioning-like behavior.
Snowsight is built for both simple and complex SQL workloads. It supports multi-statement execution, script- like workflows, stored procedure development, materialized views, and advanced analytics queries.
Unsupported options include:
* Manual data storage configuration: Snowflake abstracts storage management completely.
* Pre-built queries only: Snowsight allows fully custom SQL.
* Limited to simple queries: It supports enterprise workloads, BI query chains, and operational SQL.

NEW QUESTION # 122
What is the key difference between a "private listing" and a "public listing" in a Snowflake Data Exchange context?

A. Private listings are for sharing code, while public listings are for sharing data.
B. Private listings are shared with specific Snowflake accounts, while public listings are available to all Snowflake users.
C. Public listings are only accessible to Snowflake employees, while private listings are for external users.
D. Public listings require payment, while private listings are free.

Answer: B

Explanation:
Aprivate listingis shared only with explicitly selected Snowflake accounts, giving providers fine-grained control over who can access their data. This is ideal for partner organizations, internal business units, or customers requiring restricted access. Apublic listing, on the other hand, is accessible to all Snowflake users via the Marketplace, making it available for broad distribution. Pricing models for both types can be free or paid; neither category inherently requires payment. Both listing types share data, not code. Public listings are not restricted to Snowflake employees-they are open to the entire Snowflake ecosystem. The distinction lies entirely in access scope: targeted (private) vs. universal (public).

NEW QUESTION # 123
What is the primary purpose of Snowflake's Cloning feature?

A. To manage user permissions
B. To create a backup of the entire Snowflake account
C. To back up data in real-time
D. To create an exact copy of a database, schema, or table without duplicating the storage

Answer: D

Explanation:
Snowflake's Cloning feature provides the ability to create azero-copyclone of tables, schemas, or entire databases. This means that the cloned object references the same underlying micro-partition data as the original, so no duplicate storage costs are incurred upon creation. Instead, Snowflake uses metadata pointers to reference the existing data. If changes occur in the original or cloned objects, Snowflake performs copy-on- write operations to maintain isolation while still optimizing storage. This cloning mechanism is extremely valuable for development, testing environments, analytics validation, and troubleshooting historical states. It is not a real-time backup tool; backup and restore are handled through Time Travel and Fail-safe. Cloning does not grant or manage permissions nor copy the entire Snowflake account; it works solely at the database, schema, and table object levels.

NEW QUESTION # 124
You have a custom role named 'TRANSFORM ROLE that needs to load data into a table called
'ORDERS'. You want to ensure that this role can only load data into this specific table and has no other privileges. The table is located in database 'SALES DB' and schema 'PUBLIC'. Which of the following grants provides the LEAST PRIVILEGED access required for 'TRANSFORM ROLE' to perform the data load operation using COPY INTO command?

A. GRANT INSERT, SELECT ON TABLE SALES DB.PUBLIC.ORDERS TO ROLE TRANSFORM ROLE;
B. GRANT OWNERSHIP ON TABLE SALES DB.PUBLIC.ORDERS TO ROLE TRANSFORM ROLE;
C. GRANT INSERT ON TABLE SALES DB.PUBLIC.ORDERS TO ROLE TRANSFORM ROLE; GRANT USAGE ON DATABASE SALES DB TO ROLE TRANSFORM ROLE; GRANT USAGE ON SCHEMA SALES DB.PUBLIC TO ROLE TRANSFORM ROLE;
D. GRANT ALL PRIVILEGES ON TABLE SALES DB.PUBLIC.ORDERS TO ROLE TRANSFORM ROLE;
E. GRANT INSERT ON TABLE SALES DB.PUBLIC.ORDERS TO ROLE TRANSFORM ROLE;

Answer: C

Explanation:
The INSERT privilege is required to load data into the table. USAGE privilege on the database and schema is required to access the table. SELECT is not required for COPY INTO operations, only INSERT. OWNERSHIP grants complete control and is overly permissive. ALL PRIVILEGES also grants more privileges than needed.

NEW QUESTION # 125
Which of the following Snowflake functions is used to generate pre-signed URLs for accessing files in a stage?

A. GET_STAGE_LOCATION()
B. GET_PRESIGNED_URL()
C. BUILD_STAGE_FILE_URL()
D. GET_RELATIVE_PATH()

Answer: B

Explanation:
GET_PRESIGNED_URL() is the official Snowflake function used to create atemporary, secure, time-limited pre-signed URL, which provides controlled-access retrieval of a file stored in a Snowflake internal or external stage. Pre-signed URLs are essential for secure file sharing without exposing credentials or granting direct stage access to external users. The function accepts the stage name and file path and optionally allows specifying an expiration period. GET_STAGE_LOCATION() retrieves metadata about where a stage points, not access URLs. BUILD_STAGE_FILE_URL() constructs a basic URL but does not sign it, meaning it cannot authorize secure download access. GET_RELATIVE_PATH() provides path-only details, not a secure download link. GET_PRESIGNED_URL ensures that external consumers can access staged files safely, making it an important feature for secure distribution workflows, data export pipelines, partner integrations, and temporary file sharing scenarios.

NEW QUESTION # 126
Within a Snowflake Notebook, you have a Python script that performs several data transformations using Pandas DataFrames and then attempts to load the transformed data into a new Snowflake table. The script fails intermittently with 'MemoryError'. Which of the following strategies could you employ to mitigate the 'MemoryError' when working with Snowflake Notebooks and Python? (Choose all that apply.)

A. Increase the virtual warehouse size allocated to the Snowflake session.
B. Utilize the `COPY INTO' command directly from Snowflake to load data into the table, bypassing Pandas DataFrames entirely.
C. Optimize the Pandas DataFrame's data types to use less memory (e.g., using 'int32 instead of
'int64' where appropriate).
D. Implement chunking: Read the data from Snowflake in smaller batches, process each batch individually, and append the results to the target table.
E. Reduce the number of concurrent Snowflake Notebook sessions running.

Answer: B,C,D

Explanation:
Options B, C, and D are correct. A MemoryError indicates that the Python process within the Snowflake Notebook is running out of memory. Chunking (B) allows processing of data in smaller, manageable pieces. Optimizing Pandas DataFrame data types (C) reduces the memory footprint of each DataFrame. Using 'COPY INTO' (D) leverages Snowflake's internal data loading capabilities, avoiding the need to load the entire dataset into a Pandas DataFrame. Increasing the virtual warehouse size (A) primarily affects query performance within Snowflake, not the memory available to the Python process in the Notebook. Reducing the number of concurrent sessions might help if resource contention is the root cause, but is less directly related to the error, so E is not the strongest solution.

NEW QUESTION # 127
What MOST accurately describes Snowflake?

A. An ETL tool
B. A transactional database
C. A data warehouse software
D. An AI data cloud platform

Answer: D

NEW QUESTION # 128
What does a green checkmark next to a cell in a Snowflake notebook signify?

A. The cell has completed execution successfully
B. The cell is currently running
C. The cell has errors
D. The cell is paused

Answer: A

Explanation:
In Snowflake Notebooks, agreen checkmarkis a visual indicator that the cell hassuccessfully executed, meaning the computation completed without errors and results are available. This helps users verify execution state quickly across multi-step workflows.
Other visual states include:
* A spinning icon for cells that are currently executing
* A red error symbol for failed executions
* Plain, unmarked cells that have not yet run
Snowflake Notebooks support SQL and Python cells with integrated compute, output visualization, and cell- level execution state tracking. There is no "paused" state for notebook cells.

NEW QUESTION # 129
Which of the following are benefits of using a multi-clustered warehouse in Snowflake? (Choose any 3 options)

A. Automatic scaling of clusters based on query demand
B. Reduced storage costs
C. Increased compute capacity
D. Improved query performance during peak load

Answer: A,C,D

NEW QUESTION # 130
What Snowflake parameter is configured in the Query Processing layer?

A. The minimum and maximum micro-partition limits
B. The sizing of virtual warehouses
C. The minimum and maximum serverless compute limits
D. The types of tables available in an account

Answer: B

Explanation:
The Query Processing layer of Snowflake is wherevirtual warehouses operate, so warehouse sizing parameters (X-Small to 6X-Large) fall under this layer. Warehouse size determines compute power, concurrency, and performance behavior for SQL workloads. Administrators configure warehouse size based on workload intensity, response time requirements, and cost considerations.
Serverless compute limits and micro-partition limits belong to storage and services layers. Table types (permanent, transient, temporary) are storage-level configurations, not part of Query Processing.
Thus, warehouse sizing is the correct parameter configured at the Query Processing layer.

NEW QUESTION # 131
To exclude certain columns from a SELECT query, you should:

A. Use the OMIT clause
B. Use a REMOVE function on the table
C. Use the EXCLUDE keyword
D. Explicitly list the columns you want to include

Answer: C

Explanation:
Snowflake supports theEXCLUDEkeyword to simplify queries when excluding certain columns from a SELECT * operation. SELECT * EXCLUDE (column1, column2) reduces verbosity and enhances maintainability, especially when table schemas evolve. Explicitly listing all columns is possible but inefficient. Snowflake does not support REMOVE functions for columns nor an OMIT clause. EXCLUDE is the correct and official mechanism.

NEW QUESTION # 132
A data engineer, Bob, created a table 'CUSTOMER DATA' in schema 'SALES SCHEMA' within the database 'ENTERPRISE_DB' Bob is leaving the company, and ownership of the table needs to be transferred to Alice. Which of the following SQL commands will correctly transfer ownership of the 'CUSTOMER DATA' table to the 'ALICE role, ensuring Alice can grant further privileges on the table to other roles?

Answer: E

Explanation:
The correct command to transfer ownership in Snowflake is 'ALTER TABLE ... TRANSFER OWNERSHIP TO ROLE ..:. option A is a GRANT statement, not an ownership transfer. Option B is incorrect SQL syntax in Snowflake. Option C attempts to grant ownership to a user instead of a role, which isn't the requirement. Option D uses incorrect syntax, there is no SET OWNER property. Option E is the only command that correctly transfers ownership to a role.

NEW QUESTION # 133
Consider a scenario where you need to create a custom function (UDF) in Snowflake that uses an external Python library not available in the Snowflake Anaconda channel. How would you package and deploy this UDF, ensuring proper dependency management and version control?

A. Create a Python virtual environment, install the necessary libraries, and upload the entire environment as a ZIP file to a Snowflake stage. Then, create the UDF referencing the uploaded ZIP file.
B. Use the `snowflake-snowpark-python' library to create a Snowpark session, package the UDF and its dependencies using Conda, and deploy the resulting environment to a Snowflake stage, referencing it in the UDF definition.
C. Package the UDF code and the external library as a single Python file, upload it to a Snowflake stage, and then create the UDF referencing the uploaded file.
D. Install the required Python libraries directly on the Snowflake compute nodes using a startup script, and then create the UDF referencing the installed libraries.
E. Copy the source code of the external Python library and include it directly within the UDF definition.Snowflake will automatically resolve the dependencies.

Answer: B

Explanation:
Option D describes the recommended approach using Snowpark and Conda. This allows for proper dependency management and version control by packaging the UDF and its dependencies into a Conda environment. Options A and C lack proper dependency management.
Option B is not feasible as you cannot directly install packages on Snowflake compute nodes.
Option E is impractical and violates code organization principles.

NEW QUESTION # 134
You're using Snowflake Cortex's 'PARSE DOCUMENT function to extract data from a large number of PDF documents stored in an AWS S3 bucket. You want to optimize costs by minimizing the amount of data processed by the function. Which of the following strategies would be MOST effective? (Choose all that apply)

A. Utilize Snowflake's data skipping features by partitioning the external stage based on document type or date, allowing Snowflake to only scan the relevant files.
B. Disable Snowflake's result caching to ensure that PARSE_DOCUMENT always processes the latest version of each document.
C. Use a smaller virtual warehouse size to reduce compute costs, even if it increases the overall processing time.
D. Implement data masking policies to redact sensitive information (e.g., personally identifiable information) from the documents before parsing them.
E. Pre-process the documents to extract only the specific sections containing the information you need, storing these smaller sections in a new stage or table for parsing.

Answer: A,E

Explanation:
Option C is correct because data skipping allows Snowflake to avoid scanning unnecessary files in the external stage, reducing the amount of data that needs to be processed. Option D is correct because pre-processing reduces the total data input to the PARSE_DOCUMENT function. Option A might seem intuitive, but often a larger warehouse processes data much faster and cheaper overall due to parallelization and efficiency. Option B is relevant for security but doesn't directly minimize the amount of data processed by PARSE_DOCUMENT. Option E will increase the costs. Always review the Snowflake query history for error messages or contact Snowflake support for assistance.

NEW QUESTION # 135
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