[Apr 25, 2026] Fast2test SOL-C01 Exam Practice Test Questions (Updated 218 Questions)
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Snowflake SOL-C01 Exam Syllabus Topics:
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NEW QUESTION # 119
What is the primary purpose of the LIMIT clause in a Snowflake query?
- A. To group the result set
- B. To filter the result set
- C. To sort the result set
- D. To restrict the number of rows returned
Answer: D
Explanation:
The LIMIT clause limits the number of rows Snowflake returns from a query. It is commonly used to preview datasets, improve performance when exploring large tables, and paginate results. LIMIT may be combined with OFFSET to skip a number of rows. Sorting is performed with ORDER BY, filtering with WHERE, and grouping with GROUP BY-not with LIMIT. Therefore, restricting returned rows is the primary purpose of LIMIT.
NEW QUESTION # 120
You are tasked with securing sensitive data in a table called `CUSTOMER DATA'. You want to grant the 'SELECT privilege on this table to a role called 'ANALYST ROLE, but prevent them from seeing the 'CREDIT CARD NUMBER column, which contains personally identifiable information (PII). Which of the following approaches are valid in Snowflake to achieve this?
- A. Use dynamic data masking on the column and grant the 'UNMASK' privilege to only specific roles other than 'ANALYST_ROLE'.
- B. Create a secure view on 'CUSTOMER DATA that excludes the 'CREDIT CARD NUMBER column and grant 'SELECT on the secure view to 'ANALYST ROLE.
- C. Apply a row access policy to filter out rows containing sensitive information when accessed by
'ANALYST_ROLE. - D. Create a view on 'CUSTOMER_DATA' that excludes the column and grant 'SELECT' on the view to 'ANALYST_ROLE.
- E. Grant `SELECT on 'CUSTOMER_DATA' to 'ANALYST_ROLE and then 'REVOKE SELECT specifically on the column.
Answer: A,B,D
Explanation:
Option A is valid: Creating a view and granting 'SELECT on the view allows you to control which columns are visible to the 'ANALYST ROLE. Option B is valid: Dynamic data masking allows you to mask the sensitive column for unauthorized users (in this case, 'ANALYST_ROLE') while allowing authorized users to see the unmasked data using the 'UNMASK' privilege. Option C is invalid: You cannot revoke privileges on a specific column; privileges are granted and revoked at the table level. Option D is incorrect: Row Access Policies filter rows, not columns. Option E is valid: Secure views are the most secure way to filter the specific data.
NEW QUESTION # 121
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. Exclude all reviews with a missing 'LANGUAGE CODE from the translation process to avoid errors.
- 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. 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.
- D. Use a TRY_TRANSLATE function which does not exist, which would catch exception if LANGUAGE_CODE is invalid or Null.
- E. 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.
Answer: C
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 # 122
Which command is used to create a new virtual warehouse in Snowflake?
- A. CREATE SCHEMA warehouse_name;
- B. CREATE VIRTUALWAREHOUSE warehouse_name;
- C. CREATE WAREHOUSE warehouse_name;
- D. CREATE DATABASE warehouse_name;
Answer: C
Explanation:
Snowflake uses the SQL command CREATE WAREHOUSE warehouse_name to create a new virtual warehouse. Warehouses provide the compute resources for executing SQL queries, loading data, and performing transformations. When creating a warehouse, additional parameters may be specified, such as warehouse size, auto-suspend timeout, auto-resume, scaling policy, or multi-cluster mode. The options involving "CREATE VIRTUALWAREHOUSE" are invalid syntax in Snowflake. CREATE DATABASE and CREATE SCHEMA create storage containers, not compute resources. Therefore, CREATE WAREHOUSE is the only correct command for provisioning compute.
NEW QUESTION # 123
What is the purpose of the ACCOUNTADMIN role in Snowflake? (Choose any 2 options)
- A. To create and manage databases
- B. To grant and revoke privileges across the account
- C. To monitor query performance
- D. To manage all aspects of the Snowflake account
Answer: B,D
Explanation:
TheACCOUNTADMINrole is Snowflake's highest-privileged system-defined role. It provides complete administrative authority across the entire Snowflake account. Its functions include:
* Managing global account parameters, replication settings, business continuity, failover groups, and region configurations
* Administering billing, resource monitoring, and governance
* Granting and revoking privileges across all objects and roles
* Overseeing role hierarchy, including SECURITYADMIN and SYSADMIN
It is typically reserved for platform owners and security/governance teams, following least-privilege principles.
"Create and manage databases" is primarily a SYSADMIN responsibility.
"Monitor query performance" can be accomplished by roles with MONITOR privileges; it is not exclusive to ACCOUNTADMIN.
NEW QUESTION # 124
A company stores unstructured text data (PDFs, DOCX) in an external stage (AWS S3). They want to use Snowflake Cortex's PARSE DOCUMENT function to extract specific information, but are encountering performance issues and high costs. Which of the following strategies could optimize performance and reduce costs when using PARSE DOCUMENT in this scenario?
- A. Implement a robust error handling mechanism to prevent processing from halting due to malformed or corrupted documents and monitor the Snowflake resource consumption using Snowflake's monitoring tools.
- B. Utilize Snowflake's caching mechanism by storing parsed results in a separate table and refreshing it periodically, avoiding redundant parsing of the same documents and reduce MAX FILE SIZE to lower value like 'MAX FILE SIZE-8388608'.
- C. Pre-process the documents to remove irrelevant sections (e.g., boilerplate text, headers, footers) before loading them into Snowflake for parsing. Also, ensure appropriate partitioning of data in the external stage.
- D. Increase the size of the virtual warehouse used for processing, even if it means paying for larger compute resources, and use 'MAX in the file format configuration.
- E. Reduce the number of documents being processed in a single batch to minimize memory consumption.
Answer: A,B,C
Explanation:
Option B is correct because pre-processing reduces the amount of data that PARSE_DOCUMENT needs to process. Partitioning in the external stage enables Snowflake to more efficiently retrieve the relevant data. Option C is correct because caching prevents redundant processing and reduce MAX FILE_SIZE to lower value. Option E is correct because error handling ensures processing continues and monitoring provides insights into resource usage. Option A increasing warehouse size and MAX FILE SIZE without other optimizations is often a brute-force approach that doesn't address the root cause of performance problems and leads to unnecessary costs. Option D, limiting batch size, can help with memory issues but doesn't fundamentally improve the efficiency of document parsing.
NEW QUESTION # 125
What are the key benefits of the Snowflake multi-cluster shared data architecture? (Select TWO).
- A. It provides enhanced data security features.
- B. It allows for independent scaling of compute and storage.
- C. It stores data in a columnar format to improve performance.
- D. It offers near-unlimited concurrency and elasticity.
- E. It optimizes data loading for unstructured data.
Answer: B,D
Explanation:
The Snowflake multi-cluster shared data architecture separates compute and storage, enablingindependent scaling of each. Compute resources (virtual warehouses) operate independently from the centralized storage layer, allowing users to increase compute power without impacting storage costs-or vice versa. This flexibility provides major advantages for cost optimization and workload performance tuning.
The architecture also supportsnear-unlimited concurrency and elasticity. When many users or workloads run simultaneously, Snowflake can automatically add additional clusters to a multi-cluster warehouse, ensuring that no queries experience queuing or performance degradation. This capability is crucial for BI dashboards, ETL pipelines, and large organizations with varied usage patterns.
Columnar storage (option D) is indeed a Snowflake feature but is not unique to the multi-cluster architecture.
Enhanced security arrives via Snowflake's platform-wide mechanisms, not specifically because of multi- cluster architecture. Unstructured data loading (option B) is also not governed by multi-cluster behavior.
NEW QUESTION # 126
You need to configure Snowflake to automatically suspend a virtual warehouse named
`ANALYTICS after 10 minutes of inactivity and resume it when a query is submitted. Which of the following SQL commands achieve this?
- A. `sql ALTER WAREHOUSE ANALYTICS WH SET AUTO SUSPEND = TRUE; ALTER
WAREHOUSE ANALYTICS WH SET INACTIVITY TIMEOUT = 600 - B. ALTER WAREHOUSE ANALYTICS_WH SET AUTO SUSPEND = 600; ALTER WAREHOUSE ANALYTICS WH SET AUTO RESUME = 'ON
- C. ALTER WAREHOUSE ANALYTICS_WH SET AUTO SUSPEND = 600; ALTER WAREHOUSE ANALYTICS WH SET AUTO RESUME = TRUE;
- D. `sql ALTER WAREHOUSE ANALYTICS WH SET AUTO SUSPEND = 10; ALTER WAREHOUSE ANALYTICS WH SET AUTO RESUME = TRUE;
- E. ALTER WAREHOUSE ANALYTICS_WH SET AUTO SUSPEND = 600; ALTER WAREHOUSE ANALYTICS WH SET AUTO RESUME = TRUE; ALTER WAREHOUSE ANALYTICS WH SET AUTO RESUME INITIAL SIZE = 'MEDIUM';
Answer: C
Explanation:
The 'AUTO_SUSPEND parameter controls the number of seconds of inactivity before a warehouse is automatically suspended. 10 minutes is equal to 600 seconds. 'AUTO RESUME' set to 'TRUE' enables the warehouse to automatically resume when a query is submitted. Option B is incorrect because "INACTIVITY _ TIMEOUT does not exist. Option C is incorrect as
"AUTO_RESUME' only accepts boolean values. Option D is incorrect as auto suspend is configured for only 10 seconds. Option E is unnecessarily complicated, the core functionality is achieved by the two commands in option A. Setting initial warehouse size is an optimization, not essential for auto resume to function.
NEW QUESTION # 127
What information can be accessed using the Snowsight Monitoring tab?
- A. Query execution history
- B. Virtual warehouse usage metrics
- C. Database Time Travel snapshots
- D. Database schema changes history
Answer: B
Explanation:
The Snowsight Monitoring tab provides a centralized view of virtual warehouse usage metrics, enabling administrators and developers to evaluate how compute resources are being consumed. This includes critical insights such as credit usage, query load, concurrency levels, average queue times, execution durations, and auto-scaling activity (for multi-cluster warehouses). These metrics help determine whether a warehouse is correctly sized, whether concurrency issues are occurring, or whether workloads require scaling up or adding clusters.
Query history is available in a different section-"Activity # Query History"-not under Monitoring. Time Travel snapshots are not visualized within Monitoring; Time Travel is controlled via retention parameters and accessed with SQL (AT/BEFORE clauses). Schema change history is also not part of Monitoring and instead is discoverable through ACCOUNT_USAGE or specific metadata views.
The Monitoring tab exists specifically to help evaluate warehouse performance and resource consumption, enabling optimization of compute spending and better workload management.
NEW QUESTION # 128
You are tasked with creating a table `EMPLOYEES in Snowflake to store employee data. The table should have columns for 'employee_id' (INT, primary key), 'first_name' (VARCHAR(50)),
'last_name' (VARCHAR(50)), 'email' (VARCHAR(IOO)), and 'hire_date' (DATE). You want to ensure that when loading data, any rows with duplicate 'employee_id' values are rejected without failing the entire load . Furthermore, you need to automatically generate surrogate keys for any new departments added to the 'DEPARTMENTS' table, which is not currently populated but will be loaded later. Which of the following approaches correctly combines these requirements?
- A. Create the 'EMPLOYEES table with a unique constraint on 'employee_id' and use a COPY INTO statement with the 'ON_ERROR = SKIP_FILE' option. Use a SEQUENCE object and a DEFAULT constraint to generate department keys.
- B. Create the 'EMPLOYEES' table with 'employee_id' as the primary key. Use a COPY INTO statement with the 'ON_ERROR = CONTINUE option. Use an IDENTITY column for department keys.
- C. Create the 'EMPLOYEES' table with 'employee_id' as the primary key. Use a COPY INTO statement with the 'ON_ERROR = SKIP_FILE option. Use a SEQUENCE object and a DEFAULT constraint to generate department keys.
- D. Create the 'EMPLOYEES' table without a primary key constraint. Use a COPY INTO statement with the 'ON_ERROR = ABORT_STATEMENT option. Use a Snowflake Task to periodically check for new departments and generate keys.
- E. Create the `EMPLOYEES table with a unique constraint on 'employee_id'. IJse a COPY INTO statement with the 'ON ERROR = SKIP FILE option and VALIDATION_MODE = RETURN_ERRORS. Use an IDENTITY column for department keys.
Answer: E
Explanation:
Option E correctly addresses both requirements. Creating a unique constraint on allows Snowflake to identify duplicate rows. The = SKIP FILE' option, combined with
'VALIDATION_MODE = RETURN ERRORS', ensures that duplicate rows will not be inserted, and the COPY INTO operation will continue. Using an IDENTITY column for "DEPARTMENTS' simplifies the automatic generation of surrogate keys. Options A, B, and C will not properly handle the error situation and may stop the load completely. Option D doesn't prevent duplicates and the Snowflake Task isn't the simplest approach.
NEW QUESTION # 129
You are working with Snowflake Cortex and the COMPLETE function to generate marketing copy from product specifications. You have a table `PRODUCT SPECS containing 'PRODUCT NAME,
'KEY FEATURES, and TARGET AUDIENCE columns. You want to generate a concise and engaging marketing tagline for each product. Given that 'SNOWFLAKE.ML.COMPLETE is implemented as a secure external function, what are the MINIMUM set of steps to grant the necessary permissions and execute the function successfully (select all that apply)?
- A. Ensure external functions are enabled at the account level: ALTER ACCOUNT SET ENABLE EXTERNAL FUNCTIONS-TRUE;
- B. Grant OWNERSHIP on the SNOWFLAKE.ML.COMPLETE to the role executing the query using:GRANT OWNERSHIP ON FUNCTION SNOWFLAKE .ML. COMPLETE TO ROLE;
- C. Grant the USAGE privilege on the SNOWFLAKE schema to the role executing the query using:
GRANT USAGE ON SCHEMA SNOWFLAKE TO ROLE , - D. Grant the EXECUTE TASK privilege on the account to the role executing the query using. `GRANT EXECUTE TASK ON ACCOUNT TO ROLE
- E. Grant the USAGE privilege on the database containing the SNOWFLAKE schema to the role executing the query using: GRANT USAGE ON DATABASE SNOWFLAKE SAMPLE DATA TO ROLE
Answer: A,C,E
Explanation:
The COMPLETE function, being implemented as an external function, requires specific privileges.
Granting USAGE on the schema (A) and database (C) containing the function is essential for accessing it. Also, external functions must be enabled at the account level(D). EXECUTE TASK is unrelated to external function execution. OWNERSHIP is not needed, USAGE provides sufficient access. A and C are prereqisite to use the functions and as well Snowflake ML complete being implemented as External function, the flag ENABLE EXTERNAL FUNCTIONS should be turned on, so the correct options are
NEW QUESTION # 130
What file extension is commonly used for Snowflake notebooks?
- A. .ipynb
- B. .sql
- C. .txt
- D. .ipnb
Answer: A
Explanation:
Snowflake notebooks use the.ipynbfile extension, the standard format for Jupyter notebooks. This format stores executable code, markdown, metadata, and cell outputs in a structured JSON layout. Snowflake adopts this format to ensure compatibility with the broader Python ecosystem, thereby enabling seamless migration between Snowflake and external notebook environments.
The .ipynb structure allows mixed SQL and Python cells, visualizations, Streamlit components, documentation, and stepwise development within Snowsight. It supports reproducibility, collaboration, and integration with Snowpark and Cortex.
Incorrect formats:
* .ipnbis a misspelling and invalid.
* .sqlis used for SQL scripts only.
* .txtcannot represent notebook metadata or cell structure.
Thus, .ipynb is the correct and only supported notebook format.
NEW QUESTION # 131
Which cloud platforms does Snowflake support?
- A. Microsoft Azure
- B. All of the options
- C. Amazon Web Services (AWS)
- D. Google Cloud Platform (GCP)
Answer: B
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 # 132
What is the primary benefit of the Snowflake data cloud?
- A. It enables organizations to unite and share their data.
- B. It eliminates the need for data governance.
- C. It replaces traditional data warehouses with on-premises solutions.
- D. It provides direct access to underlying infrastructure.
Answer: A
Explanation:
The Snowflake Data Cloud allows organizations toseamlessly share, access, and collaborate on dataacross departments and external partners, without copying or moving data. Through secure data sharing, listings, and data clean rooms, Snowflake eliminates data silos and dramatically improves data collaboration.
It does not eliminate the need for governance-Snowflake enhances governance via RBAC, masking policies, and centralized controls. It does not provide access to underlying cloud infrastructure; Snowflake abstracts that. It is not an on-premises solution; Snowflake is fully cloud-native.
Thus, the primary benefit is unifying and securely sharing data across the ecosystem.
NEW QUESTION # 133
A data engineer is tasked with ensuring that only authorized personnel can access sensitive data within a Snowflake database named 'SALES DB'. They need to implement a row-level security policy that filters rows based on the current user's role. A table named 'CUSTOMER DATA exists with a column named 'REGION'. Which of the following steps is the MOST effective and secure way to achieve this, considering minimal administrative overhead and leveraging Snowflake's built-in features?
- A. Create a row access policy on the 'CUSTOMER DATA' table that filters rows based on the current user's role and the 'REGION' column.
- B. Create a stored procedure that dynamically generates SQL queries based on the current user's role and executes them against 'CUSTOMER DATA'.
- C. Create a masking policy on the 'REGION' column that replaces values with NULL based on the current user's role.
- D. Implement a custom application logic layer that retrieves all data from 'CUSTOMER_DATA' and filters it based on the user's role before displaying it.
- E. Create a view that joins `CUSTOMER DATA' with `INFORMATION SCHEMA.APPLICABLE ROLES and applies a filter based on the current user's role and the REGION' column.
Answer: A
Explanation:
Row access policies provide the most secure and manageable way to implement row-level security in Snowflake. They are tightly integrated with the database and enforce access control at the data level, reducing the risk of data leakage and simplifying administration. Options A, B, C and E are either less efficient, less secure, or more complex to manage.
NEW QUESTION # 134
What is the primary purpose of the COPY INTO command in Snowflake for data loading?
- A. To manage user roles and permissions.
- B. To create new virtual warehouses.
- C. To replicate databases across regions.
- D. To load data from staged files into a table.
Answer: D
Explanation:
COPY INTO <table> bulk-loads data from internal or external stages into Snowflake tables. It supports transformations, validation, and multiple file formats.
It does not replicate databases, manage roles, or create warehouses.
NEW QUESTION # 135
How can Snowsight be used to monitor the performance of a virtual warehouse?
- A. Use the DESCRIBE WAREHOUSE command.
- B. Use the SHOW WAREHOUSES command.
- C. Use the QUERY_HISTORY view.
- D. Use the LOAD_HISTORY view.
Answer: C
Explanation:
To monitor virtual warehouse performance in Snowsight, the correct approach is using theQUERY_HISTORYview or its Snowsight equivalent under the Activity section. QUERY_HISTORY provides detailed metrics on execution time, queueing, credit consumption, compilation time, and warehouse utilization for every executed query. By analyzing this information, users can diagnose issues such as concurrency bottlenecks, under-sized warehouses, long-running queries, or excessive I/O.
SHOW WAREHOUSES returns metadata about warehouse configurations (size, state, scaling policy) but does not provide performance data. LOAD_HISTORY tracks COPY INTO operations only and is not representative of general warehouse workload. DESCRIBE WAREHOUSE shows configuration details but no runtime or historical performance metrics.
Thus, the most accurate and complete way to assess warehouse performance-particularly execution delays, queuing, or excessive credit usage-is by using QUERY_HISTORY.
NEW QUESTION # 136
What is the default Time Travel data retention period?
- A. 45 days
- B. 90 days
- C. 1 day
- D. 7 days
Answer: C
Explanation:
The default Time Travel retention period for most objects in Snowflake is1 day(24 hours). Time Travel enables access to historical versions of data after updates, deletes, or drops. It also allows cloning databases, schemas, and tables at previous points in time, and recovering dropped objects.
While Snowflake Enterprise Edition and higher tiers allow retention periods up to 90 days, this extended window is not the default-administrators must explicitly configure it for each table, schema, or database.
Retention periods of 7, 45, or 90 days are possible only with higher service editions; the default for all accounts and objects remains 1 day unless explicitly overridden.
NEW QUESTION # 137
A data warehouse has a role hierarchy where 'ANALYST' reports to 'DATA SCIENTIST' and
'DATA SCIENTIST' reports to 'DATA ENGINEER'. The 'ANALYST' role needs to create and use user-defined functions (UDFs) within a specific schema 'ANALYTICS. However, you want to restrict the ability of `ANALYST' to grant UDF usage privileges to other roles. You also want to ensure that if a new user is granted the 'ANALYST' role in the future, they automatically have the necessary privileges to create UDFs.
Which sequence of actions would best fulfill these requirements?
- A. GRANT CREATE FUNCTION ON FUTURE FUNCTIONS IN SCHEMAANALYTICS TO ROLE ANALYST; GRANT USAGE ON SCHEMAANALYTICS TO ROLE ANALYST;
- B. GRANT CREATE FUNCTION ON SCHEMAANALYTICS TO ROLE ANALYST, GRANT USAGE ON SCHEMAANALYTICS TO ROLE ANALYST;
- C. GRANT CREATE FUNCTION ON FUTURE FUNCTIONS IN SCHEMAANALYTICS TO ROLE ANALYST; GRANT USAGE ON SCHEMAANALYTICS TO ROLE ANALYST;
- D. GRANT CREATE FUNCTION ON SCHEMAANALYTICS TO ROLE ANALYST, GRANT USAGE ON SCHEMAANALYTICS TO ROLE ANALYST; GRANT USAGE ON SCHEMAANALYTICS TO ROLE DATA SCIENTIST;
- E. GRANT CREATE FUNCTION ON FUTURE FUNCTIONS IN SCHEMAANALYTICS TO ROLE ANALYST; GRANT USAGE ON SCHEMAANALYTICS TO ROLE ANALYST; GRANT USAGE ON SCHEMAANALYTICS TO ROLE DATA SCIENTIST;
Answer: A
Explanation:
Option E is the most appropriate. It grants 'CREATE FUNCTION' on 'FUTURE FUNCTIONS' in the 'ANALYTICS' schema, meaning any new UDFs created will automatically grant the privilege.
This ensures future users with the 'ANALYST role can create UDFs. Granting USAGE' on the schema is necessary for the 'ANALYST' role to be able to access and interact with the schema, including creating UDFs. The use of 'FUTURE FUNCTIONS' grants is crucial for fulfilling the requirement of automatically granting privileges to new users. The USAGE on SCHEMA is necessary for analyst to use the database and to interact with the schema, so they can create UDFs. Option A is incorrect as it gives GRANT CREATE FUNCTION on schema, but not FUTURE functions inside that schema. Similarly Options B and D are also incorrect for this reason. Options B and D also unnecessarily grant USAGE on the schema to the DATA SCIENTIST. The data scientist already inherits privileges as part of hierarchy.
NEW QUESTION # 138
What can a schema in Snowflake contain? (Choose any 3 options)
- A. Virtual warehouses
- B. Stage
- C. Tables
- D. Views
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 # 139
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