Free Snowflake COF-C03 Practice Exam with Questions & Answers | Set: 15

The correct answer is B. Directory table .

A directory table stores file-level metadata for files in a Snowflake stage. It can be used to see a list of files available in an internal or external stage.

Why B is correct:

Directory tables allow users to query staged file metadata, including file path, file size, last modified timestamp, and file URL information.

Example:

SELECT *

FROM DIRECTORY(@my_stage);

This returns metadata for files stored in the stage.

Why the other options are incorrect:

A. A semantic view is used to define business-friendly semantic relationships for analytics, not to list staged files.

C. A materialized view stores precomputed query results, not stage file metadata.

D. Information Schema contains metadata about Snowflake objects, but the Snowflake object specifically designed to list files in a stage is a directory table.

Official Snowflake documentation reference:

Snowflake documentation describes directory tables as objects that store a catalog of staged files in cloud storage.

The correct answer is D. To unload a single file from a table into a stage .

The COPY INTO < location > command is used to unload data from Snowflake to a stage. When the SINGLE = TRUE option is included, Snowflake attempts to unload the query or table result into a single output file instead of multiple files.

Why D is correct:

SINGLE = TRUE is specifically an unload option for COPY INTO < location > . It is useful when a downstream process expects one output file.

Example:

COPY INTO @my_stage/export/customers.csv

FROM customers

FILE_FORMAT = (TYPE = CSV)

SINGLE = TRUE;

Why the other options are incorrect:

A. Preventing duplicate loads is handled by Snowflake load metadata and the FORCE option, not SINGLE = TRUE.

B. Loading an individual file is done with COPY INTO < table > and a file path or the FILES option, not COPY INTO < location > SINGLE = TRUE.

C. “Parse the results into single stages” is not a Snowflake unload concept.

Official Snowflake documentation reference:

Snowflake documentation for COPY INTO < location > describes SINGLE as the option that specifies whether to generate a single file or multiple files when unloading data.

TheRemote Disk I/Ostatistic in the Query Profile reflectstime spent waiting on remote disk access, which can occur when data needs to be retrieved from external storage (remote). This metric is crucial for identifying bottlenecks related to I/O delays, often suggesting a need for performance optimization in data retrieval paths.

The other options relate to caching and network activity, but Remote Disk I/O specifically measures the wait time for data access from remote storage locations.

Object tagging in Snowflake allows users to assign metadata labels to various database objects to improve data governance and access control. Tags can be used to categorize and manage data based on business needs, helping to enforce governance policies and streamline database administration.

Snowflake Documentation: Object Tagging

To optimize query performance on external tables, you can define a materialized view on top of the external table. Snowflake will manage and cache the result for faster retrieval.

The correct answer is A. Use the OBJECT_CONSTRUCT function with the COPY INTO < location > command .

When unloading relational data to JSON, each relational row must be transformed into a single semi-structured object. OBJECT_CONSTRUCT creates a JSON-style object from the row’s columns, and COPY INTO < location > unloads the result to a stage.

Why A is correct:

Snowflake requires unloaded JSON data to be represented as a single VARIANT, OBJECT, or ARRAY value per row. OBJECT_CONSTRUCT converts relational columns into an object suitable for JSON unload.

Example:

COPY INTO @my_stage/output/

FROM (

SELECT OBJECT_CONSTRUCT(*)

FROM customers

)

FILE_FORMAT = (TYPE = JSON);

Why the other options are incorrect:

B. Setting the file format to JSON alone is not sufficient for relational rows with multiple columns. The rows must be converted into a single semi-structured column.

C. PUT uploads local files to an internal stage. It does not unload table data.

D. GET downloads files from an internal stage to a local machine. It does not unload table data from Snowflake.

Official Snowflake documentation reference:

Snowflake documentation explains that to unload relational data to JSON, the query should return a single column containing semi-structured data, commonly produced with OBJECT_CONSTRUCT.

Resource monitors in Snowflake can perform actions such as suspending a virtual warehouse when its credit usage reaches a defined limit and triggering a notification to account administrators when credit usage reaches a specified threshold. These actions help manage and control resource usage and costs within Snowflake.

Snowflake Documentation: Resource Monitors

The correct answer is C. Casts the value from a VARIANT to a specified data type .

In Snowflake, the :: operator is the cast operator. It converts a value from one data type to another. When working with semi-structured data, it is commonly used to cast values extracted from a VARIANT column into standard SQL data types such as STRING, NUMBER, BOOLEAN, or TIMESTAMP.

Why C is correct:

A VARIANT value often needs to be explicitly cast before it is used in comparisons, calculations, or formatted output.

Example:

SELECT src:customer_id::NUMBER,

src:name::STRING

FROM my_table;

In this example:

src:customer_id::NUMBER

extracts customer_id from a VARIANT column and casts it to NUMBER.

Why the other options are incorrect:

A. :: does not specifically convert every VARIANT into an object. It casts to whichever data type is specified.

B. Extracting a value from a VARIANT uses path notation or functions such as GET and GET_PATH; :: performs casting.

D. :: has nothing to do with random sampling.

Official Snowflake documentation reference:

Snowflake documentation describes :: as the cast operator and shows its use for converting semi-structured VARIANT values to explicit SQL data types.

A materialized view in Snowflake denotes a pre-computed data set derived from a SELECT query specification and stored for later use. Unlike standard views, which dynamically compute the data each time the view is accessed, materialized views store the result of the query at the time it is executed, thereby speeding up access to the data, especially for expensive aggregations on large datasets.

The VALIDATE table function in Snowflake would typically use a unique identifier, such as a UUID_STRING, as an input argument. This function is designed to validate the data within a table against a set of constraints or conditions, often requiring a specific identifier to reference the particular data or job being validated.

In Snowflake, semi-structured data is optimally stored using specific data types that are designed to handle the flexibility and complexity of such data. TheVARIANTdata type can store structured and semi-structured data types, including JSON, Avro, ORC, Parquet, or XML, in a single column. TheARRAYdata type, on the other hand, is suitable for storing ordered sequences of elements, which can be particularly useful for semi-structured data types like JSON arrays. These data types provide the necessary flexibility to store and query semi-structured data efficiently in Snowflake.

To utilize the search optimization service in Snowflake, the correct syntax for granting privileges to a role involves specific commands that include adding search optimization capabilities:

Option C: GRANT ADD SEARCH OPTIMIZATION ON SCHEMA < schema_name > TO ROLE < role > . This command grants the specified role the ability to implement search optimization at the schema level, which is essential for enhancing search capabilities within that schema.

Options A and B do not include the correct verb " ADD, " which is necessary for this specific type of grant command in Snowflake. Option D incorrectly mentions the database level, as search optimization privileges are typically configured at the schema level, not the database level.

In Snowflake, stored procedures and User-Defined Functions (UDFs) have different invocation patterns within SQL:

The command to list all the file formats for which a user has access privileges in Snowflake isSHOW FILE FORMATS. This command provides a list of all file formats defined in the user ' s current session or specified database/schema, along with details such as the name, type, and creation time of each file format. It is a valuable tool for users to understand and manage the file formats available for data loading and unloading operations.

When a Snowflake user increases the size of a suspended virtual warehouse, the changes to compute resources are queued but do not take immediate effect. The provisioning of additional compute resources occurs only when the warehouse is resumed. This ensures that resources are allocated efficiently, aligning with Snowflake ' s commitment to cost-effective and on-demand scalability.