The correct answer is A. METADATA$ROW_ID .

Snowflake streams record change data capture, or CDC, information for tables. When a stream tracks changes to a table, Snowflake adds metadata columns that describe the type of change and identify the affected row.

The standard stream metadata columns include:

METADATA$ACTION

METADATA$ISUPDATE

METADATA$ROW_ID

METADATA$ROW_ID is a unique and immutable identifier for a row. It is used to track the row involved in a change record.

Why A is correct:

METADATA$ROW_ID identifies the row associated with a change in a stream. This helps Snowflake track changed rows in stream output.

Why the other options are incorrect:

B. METADATA$START_SCAN_TIME is not a standard stream metadata column.

C. METADATA$FILE_CONTENT_KEY is file metadata used with staged files, not standard stream change tracking.

D. METADATA$FILE_ROW_NUMBER identifies a row number in a staged file during data loading, not a stream change row.

Official Snowflake documentation reference:

Snowflake documentation explains that streams expose metadata columns such as METADATA$ACTION, METADATA$ISUPDATE, and METADATA$ROW_ID. The METADATA$ROW_ID column uniquely identifies each row involved in a change.

Access History in Snowflake is primarily utilized for compliance auditing. The Access History feature provides detailed logs that track data access and modifications, includingqueries that read from or write to database objects. This information is crucial for organizations to meet regulatory requirements and to perform audits related to data access and usage.

Role of Access History:Access History logs are designed to help organizations understand who accessed what data and when. This is particularly important for compliance with various regulations that require detailed auditing capabilities.

How Access History Supports Compliance Auditing:

By providing a detailed log of access events, organizations can trace data access patterns, identify unauthorized access, and ensure that data handling complies with relevant data protection laws and regulations.

Access History can be queried to extract specific events, users, time frames, and accessed objects, making it an invaluable tool for compliance officers and auditors.

For minimizing the cost of large tables that are used to store transitory data, which does not need to be protected against failures because it can be reconstructed outside of Snowflake, the best table type to use isTransient. Transient tables in Snowflake are designed for temporary or transitory data storage and offer reduced storage costs compared to permanent tables. However, unlike temporary tables, they persist across sessions until explicitly dropped.

Why Transient Tables:Transient tables provide a cost-effective solution for storing data that is temporary but needs to be available longer than a single session. They have lower data storage costs because Snowflake does not maintain historical data (Time Travel) for as long as it does for permanent tables.

Creating a Transient Table:

To create a transient table, use theTRANSIENTkeyword in the CREATE TABLE statement:

CREATETRANSIENTTABLEmy_transient_table (...);

Use Case Considerations:Transient tables are ideal for scenarios where the data is not critical, can be easily recreated, and where cost optimization is a priority. They are suitable for development, testing, or staging environments where data longevity is not a concern.

The correct answer is C. GLOBALORGADMIN .

Corrected typos:

The original options contain typing errors:

A. ACCOUNT AUXIN should be ACCOUNTADMIN

B. SYSAEMIN should be SYSADMIN

C. CLOBALORGADMIN should be GLOBALORGADMIN

D. HECURITYADMIH should be SECURITYADMIN

Auto-fulfillment is an organization-level capability related to Snowflake Native Apps and listings across regions or accounts. Because refresh frequency for auto-fulfilled application packages is managed at the organization/global level, the required role is GLOBALORGADMIN.

Why C is correct:

GLOBALORGADMIN is the organization-level administrative role used for managing organization-wide features. Changing the refresh frequency for an auto-fulfilled application package requires this higher-level administrative authority.

Why the other options are incorrect:

A. ACCOUNTADMIN is the highest administrative role within a Snowflake account, but auto-fulfillment refresh frequency is an organization-level setting.

B. SYSADMIN manages account objects such as warehouses, databases, schemas, and tables, but it does not manage organization-level auto-fulfillment settings by default.

D. SECURITYADMIN manages users, roles, and grants, but it is not the default role for managing auto-fulfillment refresh frequency.

Official Snowflake documentation reference:

Snowflake documentation for auto-fulfillment and organization-level administration identifies GLOBALORGADMIN as the role used for managing organization-level Snowflake features.

The correct answer is A. Set the warehouse scaling policy to ECONOMY .

A multi-cluster warehouse can use Auto-scale mode to add or remove clusters based on query concurrency. To help control credit consumption, Snowflake provides an Economy scaling policy.

Why A is correct:

The ECONOMY scaling policy is more conservative than the STANDARD scaling policy. It starts additional clusters only when Snowflake estimates there is enough query load to keep the new cluster busy for a meaningful amount of time. This helps reduce unnecessary cluster starts and can lower credit usage.

Why the other options are incorrect:

B. Setting MIN_CLUSTER_COUNT = 1 can help avoid running multiple clusters constantly, but it does not specifically control Auto-scale behavior as effectively as the Economy scaling policy.

C. Setting AUTO_SUSPEND = 0 disables automatic suspension, which can increase credit consumption.

D. Setting both minimum and maximum cluster count to 2 forces two clusters and removes Auto-scale flexibility. This can increase cost and does not control consumption efficiently.

Official Snowflake documentation reference:

Snowflake documentation explains that the Economy scaling policy conserves credits by starting additional clusters only when enough query load exists to justify them.

To improve the performance of compute-intensive queries, increasing the warehouse size (e.g., from Medium to Large or X-Large) provides more compute resources (CPU/memory) , allowing Snowflake to process queries faster. This is the most direct and effective method for performance optimization.

Snowflake supports semi-structured data types that can store complex data structures within a single column. TheVARIANTdata type can hold structured and semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML. TheARRAYdata type is also used for semi-structured data and can store an ordered list of elements. These data types enable users to work with semi-structured data directly in Snowflake without needing to flatten the data into a relational schema first.

In the Snowflake Data Exchange, the ability to get or request data from listings is generally controlled by specific roles and privileges:

A. Users with ACCOUNTADMIN role: This role typically has the highest level of access within a Snowflake account, including the ability to manage and access all features and functions. Users with this role can access data listings within the Data Exchange.

D. Users with import share privilege: This specific privilege is necessary for users who need to import shared data from the Data Exchange. This privilege allows them to request and access data listings explicitly shared with them.

The correct answer is B. Files that have already been successfully loaded into the table will be loaded again .

By default, Snowflake uses load metadata to prevent the same files from being loaded into a table more than once. Setting FORCE = TRUE overrides this behavior and reloads files even if Snowflake load metadata indicates that the files were already successfully loaded.

Why B is correct:

FORCE = TRUE tells Snowflake to ignore previous load history and load files again.

Why the other options are incorrect:

A. FORCE = TRUE is not specifically about files with unknown load status.

C. This describes the default behavior when FORCE = FALSE, not when FORCE = TRUE.

D. Loading strings longer than the target column length is controlled by other loading behavior and error handling, not by FORCE.

Official Snowflake documentation reference:

Snowflake documentation for COPY INTO < table > describes FORCE as an option that loads all files, regardless of whether they have been loaded previously.

The correct answer is C. VARCHAR(25) .

In Snowflake, CHAR, CHARACTER, STRING, and TEXT are synonyms for VARCHAR. Snowflake stores and displays these character string data types as VARCHAR.

Corrected typo:

Option D was written as CHARACTERS). The corrected option is CHARACTER(25) .

Why C is correct:

A column created as CHARACTER(25) is treated as VARCHAR(25) in Snowflake metadata and display output.

Why the other options are incorrect:

A. STRING is a synonym for VARCHAR, but Snowflake commonly displays the canonical type as VARCHAR.

B. TEXT is also a synonym for VARCHAR, but it is not the canonical display type.

D. CHARACTER is accepted when defining the column, but Snowflake displays it as VARCHAR.

Official Snowflake documentation reference:

Snowflake documentation explains that string data type synonyms such as CHAR, CHARACTER, STRING, and TEXT are equivalent to VARCHAR.

 Snowpipe is used for continuous, automated data loading into Snowflake. It uses a COPY INTO < table >  statement within a pipe object to load data from files as soon as they are available in a stage. Snowpipe does not execute UDFs, stored procedures, or insert statements. References: Snowpipe | Snowflake Documentation

https://docs.snowflake.com/en/user-guide/intro-summary-loading.html#:~:text=For%20delimited%20files%20(CSV%2C%20TSV,encoding%20to%20use%20for%20loading.

For delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets, you must explicitly specify the encoding to use for loading. For the list of supported character sets, see Supported Character Sets for Delimited Files (in this topic).

Snowpipe data load history is retained for 64 days. This retention period allows users to review and audit the data load operations performed by Snowpipe over a significant period of time, which can be crucial for troubleshooting and ensuring data integrity.

Snowflake credits are the unit of measure for the compute resources used in Snowflake. The number of credits consumed depends on the size of the virtual warehouse and the time it is running. Larger warehouses consume more credits per hour than smaller ones, and credits are billed for the time the warehouse is active, regardless of the actual usage within that time.

Secure data sharing in Snowflake allows users to share various objects between Snowflake accounts without physically copying the data, thus not consuming additional storage. Among the options provided, external tables can be shared through secure data sharing. External tables are used to query data directly from files in a stage without loading the data into Snowflake tables, making them suitable for sharing across different Snowflake accounts.