COF-C03 Exam Dumps - Snowflake SnowPro Core Certification Questions and Answers

TheSnowpark-optimized warehouseis designed with significantly higher memory per node, making it ideal formachine learning (ML) trainingand other memory-intensive workloads. Snowpark-optimized warehouses provide the necessary computational power and memory for complex, large-scale data processing tasks.

Standard and multi-cluster warehouses do not offer the same memory advantage, while the 6XL warehouse is large but not specifically optimized for memory-intensive operations.

The correct answer is B. Add more clusters to the warehouse .

Queued queries on a multi-cluster warehouse usually indicate a concurrency problem. Multiple analysts are submitting queries at the same time, and the warehouse does not have enough active cluster capacity to process them concurrently.

Why B is correct:

Adding more clusters to a multi-cluster warehouse increases concurrency capacity. In Auto-scale mode, Snowflake can start additional clusters as needed, up to the configured maximum, to reduce queuing.

Why the other options are incorrect:

A. Scheduling or limiting reports may reduce concurrency, but it adds operational overhead and does not use Snowflake’s built-in multi-cluster scaling capability.

C. Increasing warehouse size can make individual queries faster, but it does not directly improve concurrency as effectively as adding clusters.

D. A Snowpark-optimized warehouse is intended for memory-intensive Snowpark workloads, not for reducing query queues from many concurrent BI/ad hoc users.

Official Snowflake documentation reference:

Snowflake documentation explains that multi-cluster warehouses are designed to handle concurrency by automatically scaling out with additional clusters.

Snowflake ' s Cloud Services layer is responsible for managing various aspects of the platform that are not directly related to computing or storage. Specifically, it handles authorisation, ensuring that users have appropriate access rights to perform actions or access data. Additionally, it takes care of query parsing and optimization, interpreting SQL queries and optimizing their execution plans for better performance. This layer abstracts much of the platform ' s complexity, allowing users to focus on their data and queries without managing the underlying infrastructure.

The correct answer is C. HEADER = TRUE .

When unloading data with the COPY INTO < location > command, the HEADER option specifies whether column names should be included in the output file.

Why C is correct:

HEADER = TRUE includes column headings in the unloaded file output.

Example:

COPY INTO @my_stage/output/

FROM my_table

FILE_FORMAT = (TYPE = PARQUET)

HEADER = TRUE;

Important note for exam review:

For CSV unloads, HEADER = TRUE includes column names in the first row. For Parquet unloads, Snowflake can use column names in the Parquet output metadata/schema when the header option is enabled.

Why the other options are incorrect:

A. PARSE_HEADER is a file format option used when loading CSV data with inferred column names. It is not the unload option for including headers.

B. SKIP_HEADER is used when loading files to skip header rows. It does not include headers during unloading.

D. This syntax is not the correct Snowflake unload option for column headers.

Official Snowflake documentation reference:

Snowflake documentation for COPY INTO < location > describes the HEADER option as specifying whether to include table column headings in the output files.

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.

Snowflake supports a variety of subquery types, including both correlated and uncorrelated subqueries. The correct answers are B and E, which highlight Snowflake ' s flexibility in handling subqueries within SQL queries.

Uncorrelated Scalar Subqueries:These are subqueries that can execute independently of the outer query. They return a single value and can be used anywhere a value expression is allowed, offering great flexibility in SQL queries.

EXISTS, ANY/ALL, and IN Subqueries:These subqueries are used inWHEREclauses to filter the results of the main query based on the presence or absence of matching rows in a subquery. Snowflake supports both correlated and uncorrelated versions of these subqueries, providing powerful tools for complex data analysis scenarios.

Examples and Usage:

Uncorrelated Scalar Subquery:

SELECT*FROMemployeesWHEREsalary > (SELECTAVG(salary)FROMemployees);

Correlated EXISTS Subquery:

SELECT*FROMorders oWHEREEXISTS(SELECT1FROMcustomer cWHEREc.id = o.customer_idANDc.region = ' North America ' );

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 answers are C. Data is not transferred between accounts and D. Consumer account will incur compute costs .

Important correction:

The provided answer A, B is not correct.

Secure Data Sharing allows data providers to share selected database objects with consumer accounts without copying or transferring the underlying data. Consumers query the shared data using their own virtual warehouses, so the consumer generally pays for the compute used to query the share.

Why C is correct:

With Secure Data Sharing, the provider’s data remains in the provider account. Snowflake shares metadata and access, not a physical copy of the data.

Why D is correct:

Consumers use their own compute resources to query shared data. Therefore, the consumer account incurs compute costs when querying the share.

Why the other options are incorrect:

A. Regular, non-secure views generally cannot be shared in the standard secure sharing model. Views shared with consumers should be secure views, unless specific account-level settings allow non-secure objects. The general SnowPro Core concept is that secure views are used to share view logic safely.

B. Sensitive data is not automatically masked simply because data is shared. Masking policies or secure views must be designed and applied intentionally.

E. Reader accounts can query shared data but cannot edit the provider’s source data.

Official Snowflake documentation reference:

Snowflake documentation explains that Secure Data Sharing does not copy or move data between accounts. Consumers use their own warehouses to query shared data, while providers retain control of the shared objects.

The correct answer is A. View .

In Snowflake data sharing, secure objects are normally required when sharing views or functions because secure objects prevent sensitive underlying query details from being exposed. However, when the SECURE_OBJECTS_ONLY property is set to FALSE, non-secure views can be shared.

Why A is correct:

A view can be shared when SECURE_OBJECTS_ONLY = FALSE. This setting relaxes the requirement that only secure views be included.

Why the other options are incorrect:

B. Stored procedures are not standard objects shared through Snowflake secure data sharing in the same way as tables and views.

C. Storage integration access is not shared as a database object through a Snowflake share.

D. UDF sharing has security restrictions, and the safest exam answer here is a view when SECURE_OBJECTS_ONLY is disabled.

Official Snowflake documentation reference:

Snowflake documentation explains that shares can include database objects such as tables and views. Secure object requirements apply to certain shared objects, and the SECURE_OBJECTS_ONLY property controls whether only secure objects are allowed.

The correct answer is D. Contact Snowflake Support .

Important correction:

The provided answer A. Create an organization ID is not the best answer. A Snowflake organization is required for certain marketplace and sharing features, but users do not typically “create an organization ID” as the first procedural step for setting up a Data Exchange.

Why D is correct:

A Data Exchange is a private data hub that allows providers and consumers to securely share data. Creating or enabling a Data Exchange typically requires Snowflake involvement. The first step is to contact Snowflake Support or a Snowflake representative to enable or configure the Data Exchange.

Why the other options are incorrect:

A. An organization identifier may be involved in account and organization-level administration, but creating an organization ID is not the first step to create a Data Exchange.

B. CREATE LISTING is used for creating listings after the provider/share setup exists.

C. CREATE SHARE creates a share object, but it is not the first step for creating the Data Exchange itself.

Official Snowflake documentation reference:

Snowflake documentation describes Data Exchange as a controlled data sharing hub and indicates that setting up a Data Exchange requires working with Snowflake.