Snowflake recommends automating key pair rotation to improve security and comply with industry best practices. SSH key pairs are used for key-pair authentication with clients like SnowSQL or Snowpipe.

The correct answer is D. The existing micro-partitions will not be changed or impacted .

Important correction:

The provided answer B is not correct.

Snowflake stores table data in immutable micro-partitions. Because micro-partitions are immutable, Snowflake does not physically rewrite all existing micro-partitions immediately when a column is dropped. Instead, metadata is updated so that the dropped column is no longer visible or accessible in the table definition.

Why D is correct:

Dropping a column is a metadata operation. The underlying existing micro-partitions are not immediately rewritten to remove the column data. Snowflake’s immutable micro-partition design avoids expensive full-table rewrites for this type of operation.

Why the other options are incorrect:

A. Snowflake does not immediately create new micro-partitions containing only the remaining columns.

B. Existing micro-partitions are immutable and are not updated in place.

C. Snowflake does not automatically create a new table and rewrite all remaining data when a column is dropped.

Official Snowflake documentation reference:

Snowflake documentation describes micro-partitions as immutable storage units. Table metadata operations, such as dropping columns, do not immediately rewrite existing micro-partitions.

The fastest way to load a specific, discrete set of staged files is to explicitly list the file names using the FILES parameter in the COPY INTO < table > command.

Snowflake recommends using the FILES parameter when you already know the exact files that need to be loaded. This avoids the overhead of scanning and matching files using a regular expression pattern.

Why D is correct:

The FILES parameter lets you specify an exact list of staged files to load. Since Snowflake does not need to evaluate a pattern against all available staged files, this is the fastest method for loading a known, specific set of files.

Why the other options are incorrect:

A. PATTERN uses regular expression matching, which can be useful, but it is generally slower than listing specific files with FILES.

B. A path prefix can narrow the file location, but it does not explicitly identify a discrete set of files.

C. Wildcard characters are not the recommended fastest method for loading a known set of staged files in Snowflake.

Official Snowflake documentation reference:

Snowflake COPY INTO < table > documentation states that the FILES parameter is used to specify a list of one or more files to load, and Snowflake recommends using FILES instead of PATTERN for better performance when loading a specific list of files.

Snowflake supports structured data in standard tables and semi-structured data in VARIANT, OBJECT, and ARRAY columns.

Users can load and query both using standard SQL functions , without schema constraints for semi-structured formats.

The correct answer is A. GZIP .

When unloading data from Snowflake to staged files using COPY INTO < location > , the default compression for CSV output is GZIP unless a different compression option is specified.

Why A is correct:

For CSV unloads, Snowflake compresses output files using GZIP by default.

Example:

COPY INTO @my_stage/output/

FROM my_table

FILE_FORMAT = (TYPE = CSV);

If no compression is specified, the unloaded CSV files are compressed with GZIP by default.

Why the other options are incorrect:

B. AUTO is commonly used when loading data so Snowflake can automatically detect compression. It is not the default unload compression for CSV.

C. NONE disables compression, but it is not the default.

D. DEFLATE is a supported compression type, but it is not the default for CSV unload.

Official Snowflake documentation reference:

Snowflake documentation for CSV file format options explains that the default COMPRESSION value for unloading CSV data is GZIP.

The ACCESS_HISTORY view in Snowflake provides detailed information about queries executed in the account, including metadata such as the time of execution, the user, and the SQL text of the queries. This view is available in the Snowflake Enterprise edition and higher editions. The Standard edition does not include this feature.

Snowflake Documentation: Access History

Snowflake Editions: Snowflake Pricing

A VARIANT column in Snowflake is specifically designed to store semi-structured data, such as JSON, and allows for the creation of a data hierarchy. Unlike string columns, VARIANT columns can natively handle JSON data structures, enabling complex querying and manipulation of hierarchical data using functions designed for semi-structured data.

Snowflake Documentation: VARIANT Data

To see the definition of a secure view in Snowflake, the minimum privilege required is OWNERSHIP of the view. Ownership grants the ability to view the definition as well as to modify or drop the view. Secure views are designed to protect sensitive data, and thusthe definition of these views is restricted to users with sufficient privileges to ensure data security.

The GET command is used to download (i.e., unload) files from a Snowflake internal stage to the local file system . COPY INTO writes to stages, not directly to local disk.

The correct answer is B. When a collect() action is triggered .

Snowpark DataFrames are evaluated lazily. Creating a DataFrame or applying transformations does not immediately execute the query in Snowflake. Execution happens only when an action is called.

Why B is correct:

collect() is an action. When it is called, Snowpark sends the accumulated DataFrame logic to Snowflake for execution and returns the results to the client.

Example:

df = session.table( " CUSTOMERS " )

filtered_df = df.filter(df[ " REGION " ] == " WEST " )

# Query is processed when this action runs:

rows = filtered_df.collect()

Why the other options are incorrect:

A. Creating a DataFrame builds a logical plan but does not process the data.

C. Warehouse memory availability is not what triggers DataFrame execution.

D. Applying a transformation adds to the logical plan, but it does not execute the plan.

Official Snowflake documentation reference:

Snowflake documentation describes Snowpark DataFrames as lazily evaluated. Transformations are not executed until an action, such as collect(), is called.

Resource monitor schedules cannot be reset via update. The only way to restore the default schedule is to drop and recreate the monitor.