Prepare Important Exam with SOL-C01 Exam Dumps(2026)
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Snowflake SOL-C01 Exam Syllabus Topics:
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NEW QUESTION # 97
Which of the following components are included in a fully qualified name in Snowflake? (Choose any 3 options)
- A. Role name
- B. Schema name
- C. Table or view name
- D. Database name
Answer: B,C,D
Explanation:
Afully qualified namein Snowflake specifies the exact location of an object and follows this structure:
<database_name>.<schema_name>.<object_name>
This enables precise referencing, especially in environments with multiple databases or schemas.
Components:
* Database name- the top-level container.
* Schema name- the logical grouping of related objects.
* Table or view name- the specific object being referenced.
Example:
SALES_DB.PUBLIC.ORDERS
Roles arenotpart of object names. Roles control authorization, while fully qualified names handle namespace resolution. Using fully qualified names helps avoid ambiguity in pipelines, stored procedures, views, and cross-database queries.
Snowflake also supports partially qualified names depending on active context (current database and schema).
NEW QUESTION # 98
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 is paused
- D. The cell has errors
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 # 99
Which of the following statements are TRUE regarding Snowflake Marketplace listings and data sharing (Select all that apply)?
- A. Data sharing between accounts using shares consumes compute resources on the consumer's account.
- B. Data sharing between accounts using shares consumes compute resources on the provider's account.
- C. All listings on the Snowflake Marketplace are free.
- D. Consumers can request a trial period for paid listings before subscribing.
- E. A data provider can monetize their data by offering paid listings on the Snowflake Marketplace.
Answer: B,D,E
Explanation:
Paid listings are available on the Snowflake Marketplace, allowing providers to monetize data.
Consumers can often request trial periods for these listings. Standard data sharing leverages the provider's compute for data served from the share. Free listings are available, but not all listings are free. The consumer pays for compute when they create a database or access the data from provider.
NEW QUESTION # 100
What type of mode available in PARSE_DOCUMENT function? (Select two)
- A. OCR
- B. OMR
- C. LAYOUT
- D. CONTENT
Answer: A,C
Explanation:
ThePARSE_DOCUMENTfunction offers two primary processing modes:OCRandLAYOUT, each designed for different document-intelligence tasks.
OCR (Optical Character Recognition)mode extracts raw text from scanned or image-based documents. This is ideal for contracts, scanned PDFs, receipts, manuals, and any scenario where the primary goal is text extraction. OCR focuses on textual content without preserving document structure.
LAYOUTmode extracts both text and structural context, including tables, section groupings, paragraph blocks, and visual positioning. This provides layout-aware data, making it vital for downstream use cases such as Retrieval-Augmented Generation (RAG) pipelines, structured extraction, and content summarization while retaining relationships between document components.
CONTENTis not a mode-it is a JSON key in output.OMR(Optical Mark Recognition) is not supported within PARSE_DOCUMENT.
NEW QUESTION # 101
You are tasked with optimizing a frequently queried table 'SALES DATA' that experiences many updates. Which of the following Snowflake table features can significantly improve query performance in this scenario?
- A. Using a Temporary table instead of a Permanent table.
- B. Partitioning the table on the date column.
- C. Clustering the table on the most frequently filtered columns.
- D. Creating a View on top of the table.
- E. Using a Transient table instead of a Permanent table.
Answer: C
Explanation:
Clustering is the most effective way to improve query performance for frequently queried tables, especially those with updates. Clustering organizes the physical storage of data based on the clustering keys, allowing Snowflake to efficiently prune data during queries. Views do not affect the storage of the actual data, and transient/temporary tables are generally used for other purpose and will not improve the performence of the queries. Snowflake does not support Partitioning of the table by Users.
NEW QUESTION # 102
A Snowflake administrator observes slow query performance when querying data loaded from a large set of compressed CSV files stored in an external stage (AWS S3). Which of the following actions, performed in conjunction, would MOST likely improve query performance? (Choose TWO)
- A. Create a materialized view that pre-aggregates the data from the CSV files.
- B. Create a file format object that specifies the compression type used in the CSV files.
- C. Increase the virtual warehouse size used for querying.
- D. Configure the external stage to use the IAM role.
- E. Partition the data in S3 based on a commonly used filter column and update the stage definition to reflect the partitioning.
Answer: B,C
Explanation:
Increasing the virtual warehouse size (A) provides more compute resources for query execution, especially when dealing with large datasets. Defining the file format (D) with the correct compression type allows Snowflake to efficiently decompress the data during the read operation.
Materialized views (B) help but might not be appropriate for all query patterns. (C) is a security risk and doesn't directly impact performance. While partitioning data can help, this requires manually altering the file storage in S3 outside of Snowflake's control.
NEW QUESTION # 103
What can a schema in Snowflake contain? (Choose any 3 options)
- A. Virtual warehouses
- B. Stage
- C. Views
- D. Tables
Answer: B,C,D
Explanation:
Aschemain Snowflake is a logical container inside a database that groups related objects. It can containtables, views,stages,file formats,sequences,streams,tasks, and other database objects. Schemas allow structured organization, privilege management, and separation of workloads.
* Tablesstore structured data.
* Viewsare virtual tables defined by queries on other objects.
* Stagesstore data files for loading/unloading.
Virtual warehouses are compute resources, not database objects, and therefore exist outside schemas.
Warehouses belong to the account-level namespace and are managed independently of databases and schemas.
Schemas play a key role in RBAC and object naming through fully qualified names: database.schema.object.
NEW QUESTION # 104
What is the purpose of the Public Schema in Snowflake? (Choose any 2 options)
- A. Schema to store Account level meta data
- B. Storing shared resources accessible by all users
- C. Providing a default schema for new objects
- D. Storing user-specific data
Answer: B,C
Explanation:
The Public schema is created by default within every Snowflake database and serves primarily as a shared workspace that is accessible to all users who have the necessary privileges. One of its main roles is to act as adefault schemafor objects created without explicitly specifying a schema name. This ensures usability and prevents object creation errors when users forget to define a schema. Additionally, the Public schema is commonly used to storeshared or common resourcesthat multiple users or teams need to access. However, it should not be used for sensitive or user-specific data because access can be broad. It does not store account- level metadata; that information resides in INFORMATION_SCHEMA views or account-level metadata functions. Overall, the Public schema acts as both a convenient default location and a shared-access area within a Snowflake database.
NEW QUESTION # 105
Which data type in Snowflake is commonly used for storing semi-structured data?
- A. VARIANT
- B. BLOB
- C. VARCHAR
- D. JSON
Answer: A
Explanation:
Snowflake uses theVARIANTdata type as the native mechanism for storing semi-structured data formats such as JSON, XML, Avro, ORC, and Parquet. VARIANT supports hierarchical, nested structures and allows querying via dot or bracket path notation without requiring schema-on-write.
JSON is not a Snowflake data type-JSON documents arestored insideVARIANT.
VARIANT allows:
* Mixed structured and semi-structured content
* Efficient storage through Snowflake's columnar compression
* Schema-on-read flexibility
* Querying nested structures without flattening
VARCHARstores plain text only.
BLOBis reserved for binary data and not suitable for interactive querying.
This design eliminates the need for preprocessing or schema enforcement at ingestion time.
NEW QUESTION # 106
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?
- A. Prefix each SQL statement with 'TRY and include a 'CATCH' block at the end to handle any exceptions and perform a rollback if needed.
- B. Use the 'snowflake.connector' Python library to explicitly manage transactions using , followed by individual SQL statements, and or based on success or failure.
- C. 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.
- D. Wrap the SQL statements within a 'BEGIN' and 'END' block within the cell. Snowflake Notebooks automatically treat this as a transaction.
- E. Snowflake Notebooks do not support transactions within a single cell. Each SQL statement is executed independently.
Answer: C
Explanation:
Snowflake Notebooks' cell does not directly support BEGIN...END transaction block. While the
'snowflake.connector' can manage transactions, it typically requires multiple cells for BEGIN, statements, and COMMIT/ROLLBACK. TRY/CATCH is not a valid SQL construct for transaction management. Option D is incorrect because transactions are possible using stored procedures called from a cell. The most reliable method is to create a stored procedure (E) that handles transaction management and call it from the notebook cell. This encapsulates the logic and ensures proper rollback behavior.
NEW QUESTION # 107
Which of the following data format supported to unload semi-structured data from Snowflake? (Choose any 2 options)
- A. JSON
- B. Parquet
- C. CSV
- D. XML
Answer: A,B
Explanation:
Snowflake supports unloading semi-structured data specifically intoJSONandParquetformats. JSON preserves semi-structured hierarchy and nested fields, making it ideal for interchange, backup, and integration with downstream systems. Parquet provides columnar, compressed, and metadata-rich storage well-suited for analytical reprocessing in data lakes or other systems. CSV is not semi-structured and cannot represent nested data accurately. XML, although a semi-structured format, is not supported by Snowflake as an unload format.
When performing COPY INTO <location>, users can specify FILE_FORMAT with TYPE=JSON or TYPE=PARQUET to export semi-structured content efficiently.
NEW QUESTION # 108
A data engineer has created a custom role `ETL ROLE responsible for loading data into a Snowflake warehouse. This role needs to be able to truncate tables within a specific schema
`RAW DATA. Which of the following is the most secure and appropriate way to grant this privilege to 'ETL ROLE?
- A. GRANT TRUNCATE ON ALL TABLES IN SCHEMA RAW DATA TO ROLE ETL ROLE;
- B. GRANT TRUNCATE ON FUTURE TABLES IN SCHEMA RAW DATA TO ROLE ETL ROLE;
- C. GRANT ALL PRIVILEGES ON SCHEMARAW DATA TO ROLE ETL ROLE;
- D. GRANT USAGE ON DATABASE TO ROLE ETL ROLE; GRANT TRUNCATE ON ALL TABLES IN SCHEMA RAW DATA TO ROLE ETL ROLE;
- E. GRANT USAGE ON DATABASE TO ROLE ETL ROLE; GRANT TRUNCATE ON FUTURETABLES IN SCHEMA RAW DATA TO ROLE ETL ROLE;
Answer: E
Explanation:
Option E provides the most secure and flexible approach. It grants 'USAGE on the database (required to access the schema) and then grants the 'TRUNCATE' privilege on future tables in the specified schema. This means that any new tables created in the 'RAW_DATA' schema will automatically inherit the TRUNCATE privilege for the 'ETL_ROLE'. This avoids the need to manually grant `TRUNCATE each time a new table is created. Using 'future grants' ensures maintainability. Option A grants all privileges on the schema, violating the principle of least privilege. Option B only grants truncate on currently existing tables. Option D grants truncate on ALL tables in the RAW DATA schema. It doesn't grant truncate to tables that don't currently exist but may exist in the future.
NEW QUESTION # 109
What is the name for a Snowflake account holder who shares data with other Snowflake accounts?
- A. A data administrator
- B. A data consumer
- C. A data provider
- D. A data reader
Answer: C
Explanation:
In Snowflake's Secure Data Sharing model, the accountsharingdata is known as thedata provider. Providers create shares that include selected objects (tables, views, secure views, etc.) and grant access to consumer accounts.
Adata consumerreceives the shared data.Data readeranddata administratorare not defined terms in Snowflake' s sharing architecture.
NEW QUESTION # 110
A Snowflake administrator wants to ensure that all newly created schemas in a specific database automatically inherit certain grants (e.g., 'SELECT' privilege on all tables in the schema to a reporting role). Which of the following approaches is the MOST suitable to accomplish this?
- A. Use the 'DEFAULT_PRIVILEGES' parameter at the database level to automatically grant the desired privileges on future schemas and their objects.
- B. Manually execute GRANT FUTURE GRANTS' statements after each schema creation to grant the desired privileges.
- C. Implement a custom event listener that triggers upon schema creation and executes the necessary GRANT statements.
- D. Create a stored procedure that is automatically executed after each 'CREATE SCHEMA' statement using a Snowflake Task.
- E. Schedule a periodic Snowflake Task to search for recently created schemas and grant needed permissions.
Answer: A
Explanation:
'DEFAULT PRIVILEGES is the intended and most efficient mechanism for automatically granting privileges on future objects within schemas. Options A, C, D, and E are more complex and less efficient. Option D requires manual execution each time. Option A requires more overhead.
Option C is not available in Snowflake. Option E has a delay before new permissions are granted and can be a performance overhead.
NEW QUESTION # 111
How is the initial activation of the Snowflake Data Exchange feature, which is necessary for establishing private exchanges, typically accomplished?
- A. Any Snowflake account administrator can enable Data Exchange with a click.
- B. Activation requires a SQL command executed by ACCOUNTADMIN.
- C. Activation requires a request to Snowflake Support.
- D. Data Exchange is automatically enabled for all accounts.
Answer: C
Explanation:
Snowflake Data Exchange is not enabled by default. Accounts must open a support request to Snowflake Support, which then activates the feature. It cannot be enabled via UI or SQL. After activation, administrators may create private exchanges and listings.
NEW QUESTION # 112
Which statement is true regarding internal stages?
- A. Internal stages can only be used for structured data
- B. Internal stages are managed and stored within Snowflake
- C. Internal stages are accessed with external cloud provider credentials
- D. Internal stages can only be used for semi-structured data
Answer: B
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 # 113
A data warehouse contains a table 'SALES TRANSACTIONS that is frequently updated. You need to ensure that the data is protected against accidental deletions and updates, and you need to be able to restore the data to a specific point in time. Which of the following Snowflake features and configurations should you implement to achieve this goal effectively?
- A. Set the `DATA RETENTION TIME IN DAYS' parameter to a sufficiently long period (e.g., 90 days) and regularly create backups using Snowflake's data export feature.
- B. Enable replication across multiple regions to protect against regional outages and ensure data availability. This is the only protection required.
- C. Set the `DATA RETENTION TIME IN DAYS' parameter at the account level to a sufficient value, and understand that Fail-safe provides an additional layer of protection, although not directly accessible.
- D. Rely solely on Fail-safe as this guarantees long-term data recovery and provides automated recovery capabilities. Set Data Retention parameter to 1.
Answer: C
Explanation:
Setting the parameter enables Time Travel, which allows restoring data to a specific point in the past within the retention period. Fail-safe provides an additional layer of protection beyond Time Travel, but it's not directly accessible for user- initiated recovery. Replication (Option A) is for disaster recovery but doesn't help with user errors. Backups (Option B) are useful but add complexity compared to Time Travel. Option D relies solely on Fail-safe, which is not user- accessible. Note, it is best to only provide time travel on required databases to save money on storage costs, account level might be unnecessary. Therefore, C is still better option than setting at table level also.
NEW QUESTION # 114
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