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SOL-C01 exam torrent Snowflake study guide
Snowflake SOL-C01 Exam Syllabus Topics:
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NEW QUESTION # 32
What file formats can be loaded into Snowflake using the COPY INTO command? (Choose any 3 options)
- A. Parquet
- B. CSV
- C. JSON
- D. XLS
Answer: A,B,C
Explanation:
TheCOPY INTOcommand supports loading structured and semi-structured data from various Snowflake stages. Supported file formats includeCSV,JSON,Parquet,Avro,ORC, and others. Each format integrates with Snowflake's file format definitions, enabling configuration for delimiters, compression, headers, and parsing rules.
* CSVis widely used for tabular datasets and supports flexible parsing via FILE_FORMAT options such as FIELD_DELIMITER, SKIP_HEADER, and NULL_IF.
* JSONis loaded natively into VARIANT columns, allowing nested structures to be queried immediately through path notation.
* Parquet, a high-efficiency columnar format, is optimized for analytic workloads and provides excellent compression and schema evolution benefits.
XLS is not supported, as Snowflake does not support Excel spreadsheets directly. XLS/XLSX files must be converted to CSV or Parquet before loading.
The COPY INTO architecture ensures performant ingestion across multi-node compute clusters.
NEW QUESTION # 33
A data analyst wants to clone the 'SALES DB' database to create a development environment called "DEV SALES DB'. The analyst needs to ensure that all schemas within 'SALES DB' , including custom schemas with data, are replicated in the clone, and that the clone operates as a completely independent database. Which of the following sequence of commands is the most efficient and reliable way to achieve this?
- A. CREATE DATABASE DEV SALES DB CLONE SALES DB;
- B. CREATE OR REPLACE DATABASE DEV SALES DBAS COPY OF SALES DB;
- C. CREATE DATABASE DEV SALES DB CLONE SALES DB COPY GRANTS;
- D. BACKUP DATABASE SALES DB TO S3; RESTORE DATABASE DEV SALES DB FROM S3;
- E. CREATE DATABASE CREATE SCHEMA CREATE SCHEMA Repeat for all schemas in SALES_DB; Manually copy data into each table.
Answer: A
Explanation:
'CREATE DATABASE CLONE SALES_DB;' is the correct and most efficient way to clone a database in Snowflake. It creates a point-in-time snapshot clone of the entire database, including all schemas, tables, and other objects. Option A is manual and highly inefficient. Option C involves backups and restores, which are not the intended approach for cloning within Snowflake.
Option D's syntax is incorrect as 'AS COPY OF is not a valid clause, and 'COPY GRANTS' with clone may not be relevant here.
NEW QUESTION # 34
What parameter is used to define how long Time Travel can be used to access a table?
- A. TIMEZONE
- B. DATE_OUTPUT_FORMAT
- C. USE_CACHED_RESULT
- D. DATA_RETENTION_TIME_IN_DAYS
Answer: D
Explanation:
The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for Snowflake objects such as tables, schemas, and databases. It specifies the number of days that historical data (prior versions of rows or dropped objects) is retained and accessible via Time Travel. Within this retention window, users can query data "as of" a previous time, restore dropped objects, or clone objects at a historical point.
DATE_OUTPUT_FORMAT determines the display format for date values and is unrelated to historical retention. TIMEZONE affects how timestamps are interpreted and displayed, not how long data history is preserved. USE_CACHED_RESULT governs whether Snowflake may return cached query results, not Time Travel behavior.
NEW QUESTION # 35
You're using a Snowflake Notebook to collaboratively develop a data pipeline. Several team members are working on the same notebook concurrently. One team member accidentally deletes a cell containing critical data transformation logic. What Snowflake features, accessible within or through the Notebook environment, can be used to recover the lost cell's content and minimize disruption to the workflow? Select TWO correct answers.
- A. Use the 'Undo' function within the Snowflake Notebook editor to revert the deletion. If the 'Undo' history is insufficient, manually re-enter the code from memory or documentation.
- B. Since Snowflake Notebooks automatically back up cell contents to a secure cloud storage location, contact Snowflake support to request a restoration of the deleted cell.
- C. Check the Snowflake Notebook's version history (if enabled) to revert to a previous version of the notebook before the cell was deleted.
- D. Examine the Snowflake query history associated with the notebook session. The SQL statements executed in the deleted cell may be present in the query history, allowing you to reconstruct the code.
- E. Leverage the Snowflake Time Travel feature on the underlying table(s) used in the deleted cell's logic to retrieve the data at a point in time before the deletion occurred, then recreate the cell with the recovered data transformation logic.
Answer: C,D
Explanation:
Snowflake Notebook version history (C) is the primary mechanism for reverting to a previous state. The Snowflake query history (D) will contain the SQL commands executed by the cell, allowing for reconstruction of the logic. 'Undo' (A) is limited and may not be sufficient. Time Travel (B) applies to data , not code in the notebook. Snowflake does not automatically back up individual cells for restoration (E).
NEW QUESTION # 36
What task can be performed on the Snowsight Schema Details page?
- A. Truncate the schema data.
- B. Change the schema name.
- C. Share a schema with a different account.
- D. Edit the schema metrics.
Answer: B
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 # 37
What is the purpose of the "auto-suspend" feature in Snowflake virtual warehouses?
- A. To automatically add more nodes to the warehouse
- B. To automatically shut down the warehouse after a period of inactivity
- C. To automatically increase the size of the warehouse during peak load
- D. To automatically reduce the number of clusters
Answer: B
Explanation:
The auto-suspend feature saves compute costs byautomatically suspending a warehouse when it becomes idlefor a configured period. Since Snowflake charges for compute time while a warehouse is running, auto- suspend prevents unnecessary credit consumption by stopping the warehouse when no queries are executing.
The warehouse can resume automatically when a new query is submitted, provided auto-resume is enabled.
Auto-suspend does not change warehouse size (scaling up/down) nor adjust clusters in a multi-cluster warehouse (scaling in/out). It strictly controls when compute resources turn off due to inactivity, making it an essential cost-optimization feature.
NEW QUESTION # 38
A data scientist is working on a machine learning project using Snowflake Notebooks. They have a dataset stored in Snowflake and need to perform feature engineering. They want to write a Python function that takes a Snowflake table name and a list of SQL expressions as input, executes these expressions against the table, and returns a Pandas DataFrame containing the new features. Which approach is MOST suitable for creating and executing this function within a Snowflake Notebook, minimizing data transfer outside of Snowflake?
- A. Use the Snowflake web UI to create a view containing the feature engineered data. Load that view into the notebook as a Pandas DataFrame.
- B. Use the `snowflake.snowpark.functions.call_udf to call a UDF from Snowflake notebooks and create a dataframe.
- C. Use the `snowflake.connectors library to connect to Snowflake, execute each SQL expression using the method, and append the results to a Pandas DataFrame.
- D. Create a Snowflake User-Defined Function (UDF) that encapsulates the feature engineering logic, and then call this UDF from the notebook using Snowpark or standard SQL.
- E. Utilize the `sqlalchemy' library to establish a connection to Snowflake, construct the SQL query dynamically using SQLAlchemy's expression language, and fetch the results into a Pandas DataFrame.
Answer: D
Explanation:
Creating a UDF is the most efficient way to perform feature engineering because the computation happens within Snowflake's compute engine. Snowpark's or standard SQL can then be used to call and retrieve data from UDFs. This minimizes data transfer. Option A still fetches intermediate data. Options D create extra steps and extra objects. Option E isn't the standard approach when working with Snowpark in Notebooks.
NEW QUESTION # 39
What is the name for a Snowflake account holder who shares data with other Snowflake accounts?
- A. A data administrator
- B. A data provider
- C. A data consumer
- D. A data reader
Answer: B
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 # 40
You have a Snowflake table named 'products' with columns 'product id', 'product_name', and
'price'. You need to create a view called 'expensive_products' that only shows products with a price greater than $100. However, you also want to ensure that only users with the 'PRODUCT VIEWER' role can access this view. What SQL statements are required to achieve this?
- A. Option E
- B. Option D
- C. Option B
- D. Option C
- E. Option A
Answer: E
Explanation:
Option A correctly creates the view with the required filtering and grants 'SELECT privilege to the
'PRODUCT VIEWER role. Option B includes 'WITH GRANT OPTION' which allows the to grant SELECT privileges to other roles, which is not a requirement here. Option C includes granting USAGE on Database, this might be required in your enviroment, so its not wrong, but not directly answering the question, so not ideal. Option D incorrectly uses 'ALTER VIEW expensive_products SET SECURE;' a Secure View, this is used to prevent the view definition from being visible. Option E grants 'ALL' privileges, this is not required, only `SELECT privilege is requested
NEW QUESTION # 41
How does Snowflake process queries?
- A. By optimizing data in cloud storage
- B. Using MPP compute clusters
- C. With shared-disk architecture
- D. Through third-party connectors
Answer: B
Explanation:
Snowflake processes queries usingMassively Parallel Processing (MPP)compute clusters, deployed as virtual warehouses. Each warehouse consists of multiple compute nodes working in parallel to execute queries efficiently. When a query is submitted, Snowflake distributes tasks across nodes, processes data subsets concurrently, and aggregates results. This architecture enables high performance, scalability, and the ability to handle complex analytical workloads. While Snowflake does incorporate elements of shared-disk storage, query execution itself depends on MPP compute clusters. Options such as third-party connectors or storage optimization do not represent the core query processing mechanism.
NEW QUESTION # 42
Where do tables reside in Snowflake?
- A. Schemas
- B. Stages
- C. File formats
- D. Virtual warehouses
Answer: A
Explanation:
In Snowflake's logical hierarchy, tables reside within schemas, and schemas reside within databases. This structured arrangement allows clear organization, access management, and separation of workloads or business domains. A schema functions as a container that holds tables, views, functions, sequences, stages, and file formats. When users reference a table, they typically qualify it using the full three-part name:
database.schema.table.
Stages are used to store or reference files, not to hold structured database tables. Virtual warehouses provide compute power for executing queries and performing DML operations but do not store data. File formats define how staged files should be interpreted (CSV, JSON, Parquet, etc.) and are unrelated to where tables physically reside.
Snowflake separates compute and storage, so while compute happens in virtual warehouses, all persistent data-including tables-is managed in Snowflake's centralized storage layer, organized through schemas and databases. This organization ensures consistency, ease of management, and predictable access control.
NEW QUESTION # 43
When loading CSV data, what FILE FORMAT option allows you to skip the header row?
- A. IGNORE_HEADER = TRUE
- B. SKIP_HEADER = 1
- C. HEADER_SKIP = 1
- D. SKIP_FILE_HEADER = FALSE
Answer: B
Explanation:
Snowflake's file format options allow granular control over CSV ingestion. The SKIP_HEADER = 1 parameter instructs Snowflake to ignore the first line of the file, typically containing column headers. This is the correct and official parameter within FILE FORMAT objects or inline COPY INTO options. Other options listed-such as SKIP_FILE_HEADER, IGNORE_HEADER, or HEADER_SKIP-are not valid Snowflake parameters. When SKIP_HEADER is applied, Snowflake begins reading data rows starting from line 2. This ensures proper alignment between file contents and table structure during ingestion.
SKIP_HEADER can also be set to values greater than 1 if multiple header rows exist. This behavior is essential when loading structured data where the header row is not intended for table consumption.
NEW QUESTION # 44
You are using Snowpipe to load data into a table. You need to monitor the data loading process and identify any files that failed to load. Which of the following actions will provide you with information about the files that were processed by Snowpipe and their loading status, including error messages?
- A. Query the 'VALIDATION MODE column in the target table to see if the data was successfully loaded and if any validation errors occurred during the loading process.
- B. Query the `COPY HISTORY' view in the `SNOWFLAKE database to view the history of COPY INTO statements executed by Snowpipe, including the files loaded and any error messages.
- C. Enable the ERROR copy option to log all errors to a separate table. Query this table to identify the files that failed to load and their corresponding error messages.
- D. Examine the event logs generated by the cloud provider's event notification service (e.g., AWS SNS) that triggers Snowpipe. These logs contain detailed information about the files processed and any errors encountered.
- E. Use the 'SYSTEM$PIPE STATUS function to check the status of the Snowpipe. If the status is
'ERROR', examine the error logs in the cloud storage service (e.g., AWS S3) for detailed error messages.
Answer: B
Explanation:
The 'COPY HISTORY view in the 'SNOWFLAKES database provides detailed information about the COPY INTO statements executed by Snowpipe, including the files loaded, the number of rows loaded, and any error messages encountered during the loading process. This allows you to identify files that failed to load and understand the reasons for the failures. Option A is incorrect,
'VALIDATION_MODE' isn't a column. Option B provides general status, not file-specific details.
Option D 'ON_ERROR can redirect bad records, but doesn't provide a history like COPY_HISTORY. option E relies on external logging, while `COPY_HISTORY is native to Snowflake.
NEW QUESTION # 45
What is the CREATE FILE FORMAT command used for in Snowflake?
- A. To define the format of data files for processing
- B. To delete a file format
- C. To create a new table
- D. To modify an existing file format in a table
Answer: A
Explanation:
The CREATE FILE FORMAT command creates a file format object in Snowflake that defines how data files should be interpreted when loading or unloading. Parameters in the definition include file type (for example, CSV, JSON, PARQUET), field delimiters, compression, quoting rules, and other parsing options. Once created, this file format object can be referenced by COPY INTO commands or external table definitions to ensure consistent handling of files.
Deleting a file format is performed using DROP FILE FORMAT. Modifying an existing file format is done with ALTER FILE FORMAT. Creating a new table is achieved with CREATE TABLE. Therefore, the purpose of CREATE FILE FORMAT is specifically to define how Snowflake should process data files.
NEW QUESTION # 46
What is a Reader Account in the context of Snowflake Data Sharing?
- A. A restricted account used for internal Snowflake operations.
- B. A standalone account provided by a data provider to a consumer who does not have a Snowflake account.
- C. An account primarily used for data replication.
- D. An account that can only read data from external stages.
Answer: B
Explanation:
A Reader Account allows data providers to share data with recipients who do not have their own Snowflake subscriptions. Providers manage and pay for compute and storage used by the reader account. Reader accounts are limited to querying shared data and cannot alter Snowflake objects. They are not for replication, internal operations, or external stage reading.
NEW QUESTION # 47
A security auditor has identified that several users have been granted the role directly. Your company's security policy mandates that 'ACCOUNTADMIN' should only be used in emergency scenarios and access should be granted temporarily through a subordinate role. Which of the following steps should you take to remediate this situation while minimizing disruption to existing user workflows? (Choose two)
- A. Create a new custom role (e.g., 'ADMIN_ROLE) and grant ` ACCOUNTADMIN' to this role. Then grant this role with 'GRANT ROLE ADMIN_ROLE TO USER
- B. Create a new custom role (e.g., 'ADMIN ROLE) and grant it all the privileges currently held by the
'ACCOUNTADMIN' role. - C. Create a new custom role (e.g., 'ADMIN ROLE') and grant it 'OWNERSHIP on the account. Grant the 'ADMIN ROLE' to the users who currently have ACCOUNTADMIN'.
- D. Create a new custom role (e.g., 'ADMIN ROLE) and grant it the 'ACCOUNTADMIN' role. Then grant the 'ADMIN ROLE to the users who currently have ACCOUNTADMIN'.
- E. Revoke the 'ACCOUNTADMIN' role from all users and inform them that they should not be using this role anymore. If they have access requirement, contact sysadmin.
Answer: A,D
Explanation:
Option A: Creating a subordinate role and granting it the 'ACCOUNTADMIN' role is the correct approach. This allows you to control access to through a more manageable role. Option D is how the grant should be implemented. Option B is incorrect because the complete set of
'ACCOUNTADMIN' privileges is complex and subject to change; using GRANT ROLE ensures the custom role always inherits the full 'ACCOUNTADMIN' permissions. Option C would disrupt users' workflows significantly. Option E OWNERSHIP on Account is not the intended role
NEW QUESTION # 48
Where is unstructured data stored in Snowflake?
- A. In the Cloud Services layer
- B. In external tables
- C. In tables with a single VARCHAR column
- D. In internal or external stages
Answer: D
Explanation:
Unstructured data such as PDF files, images, and other binary documents is stored in stages in Snowflake.
These stages may be internal stages, which Snowflake manages directly, or external stages, which reference external cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. Stages are the designed mechanism for storing and accessing unstructured files so that they can be processed with functions like PARSE_DOCUMENT or accessed via directory tables.
External tables are used to query structured or semi-structured data (for example, Parquet or JSON) stored in external locations, not to store raw unstructured binary content. The Cloud Services layer coordinates metadata, security, and query services; it does not store user data. Tables with a single VARCHAR column might be used as an improvised approach for small text blobs, but this is not the native or recommended method for managing unstructured data at scale.
NEW QUESTION # 49
How are privileges inherited in the Snowflake role hierarchy?
- A. Privileges are inherited by any roles at the same level in the hierarchy.
- B. Privileges are only inherited by the direct parent role in the hierarchy.
- C. Privileges are only inherited by direct child roles in the hierarchy.
- D. Privileges are inherited by any roles above a given role in the hierarchy.
Answer: D
Explanation:
Snowflake uses a hierarchical Role-Based Access Control (RBAC) model. When Role A is granted to Role B, the higher-level role (Role B) inherits all privileges of the lower-level role (Role A). This upward inheritance allows broad administrative roles (such as SYSADMIN) to automatically inherit permissions granted to subordinate roles.
Key principle:privilege inheritance flows upward, not downward.
* Parent roles inherit from child roles.
* Child roles donotinherit from parent roles.
Sibling roles do not share privileges unless explicitly granted.
Thus, roles above a given role in the hierarchy inherit privileges, making option C correct.
Options A and D incorrectly describe downward inheritance, which Snowflake does not perform.
Option B (same-level inheritance) also does not occur unless privileges are explicitly granted.
This hierarchical model simplifies governance by allowing higher-level administrative roles to centrally manage access across multiple subordinate roles.
NEW QUESTION # 50
What is the purpose of auto-resume on a virtual warehouse?
- A. To automatically start the warehouse when a query is submitted to it.
- B. To prevent the warehouse from suspending.
- C. To automatically scale up the warehouse size.
- D. To restart the warehouse manually after suspension.
Answer: A
Explanation:
When auto-resume is enabled, Snowflake automatically starts a suspended warehouse when a query or task is submitted. This ensures smooth operations without manual intervention.
It does not scale the warehouse, prevent suspension, or require manual restarting.
NEW QUESTION # 51
You are developing a Snowflake Notebook to analyze sales data. You need to connect to a Snowflake database using Python and execute a query to retrieve the top 10 products by sales volume. Which of the following code snippets is the MOST efficient and secure way to achieve this, assuming you've already configured the necessary connection details and have
'snowflake.connector' installed?
- A.
- B.
- C.
- D.
- E.
Answer: E
Explanation:
Option E is the most secure and robust. It utilizes environment variables to store credentials securely, includes error handling for potential query execution issues, and ensures that the cursor and connection are closed properly in a `finally' block, even if an error occurs. Option A hardcodes credentials, which is a major security risk. Options B and C don't include comprehensive error handling. Option D uses 'fetchmany(10)' , which might not fetch all results if there are exactly 10 rows and is less common for retrieving all rows.
NEW QUESTION # 52
You are loading data from a CSV file stored in an Amazon S3 bucket into a Snowflake table named `CUSTOMER DATA. The CSV file contains a header row, and the data is comma- separated. The 'CUSTOMER DATA table has columns `customer_id', 'first_name', and 'email'.
You want to use a named file format object called `CSV FORMAT which you have already created. You also want to skip the header row and only load data where the column is not null.
Which of the following `COPY INTO' statement achieves this most efficiently and correctly?
- A.
- B.
- C.
- D.
- E.
Answer: D
Explanation:
Option D is the most correct because it efficiently copies the data from the stage, using a named file format, skips the header and sets error handling. The WHERE clause to filter null emails cannot be directly used within COPY INTO. Options A, B and C, is incorrect, as 'WHERE clause and 'SKIP_HEADER = 1' is not applicable as the way written in the option. E is incorrect as its using select statement and it cannot be used in 'COPY INTO'
NEW QUESTION # 53
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