Free Snowflake COF-C02 Practice Exam with Questions & Answers

 A user with the OWNERSHIP privilege on a user object or the CREATE USER privilege on the account can create or manage other users in a Snowflake account56.

When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.

A directory table is a special Snowflake-managed table that stores metadata about unstructured data files (such as images, videos) in a stage.

It supports automatic metadata refresh.

It enables listing and querying file-level details.

In the Snowflake Web Interface (UI), the Query History page displays the history of queries executed in Snowflake for up to 14 days. This allows users to review and analyze their query performance, troubleshoot issues, and understand their query patterns over a two-week period. The Query History page is a critical tool for monitoring and optimizing the use of Snowflake.

Comprehensive and Detailed Explanation From Exact Extract:

Why B (Time Travel will be deactivated for the object) is correct:In Snowflake, the data retention period determines how long historical data (including deleted or modified data) is retained for Time Travel. When this period is set to 0 days, Time Travel is effectively disabled for that object (such as a table, schema, or database).

From the official Snowflake documentation:

“The DATA_RETENTION_TIME_IN_DAYS parameter specifies the number of days for which historical data is retained for Time Travel. A value of 0 disables Time Travel for the object.”

This means:

When DATA_RETENTION_TIME_IN_DAYS = 0, no historical versions of the object’s data are retained.

As a result, Time Travel queries (using AT or BEFORE clauses) and undrop/recovery operations are not available for that object.

Why A (Streams containing the object will become stale) is incorrect:Streams become stale when the data change tracking window exceeds the retention period of the source table’s Time Travel, but only if the retention period expires after some time. Setting retention to 0 disables Time Travel immediately, but doesn’t by itself make existing streams “stale.” Instead, new streams cannot be used effectively because there is no historical data to track.

Why C (Data replication will be disabled) is incorrect:Data replication is independent of the Time Travel retention period. A retention period of 0 does not affect replication; it only impacts the Time Travel feature.

Why D (Data sharing will be disabled) is incorrect:Data sharing is a separate Snowflake feature (using Secure Data Sharing). It is not impacted by Time Travel retention settings. Shared data can still be accessible regardless of the retention period.

References (Snowflake SnowPro Core / Study Guide / Official Documentation):

Snowflake Documentation — Time Travel:

“Setting DATA_RETENTION_TIME_IN_DAYS = 0 disables Time Travel for the object.”

Snowflake Documentation — Object Parameters:Describes DATA_RETENTION_TIME_IN_DAYS behavior at the database, schema, and table levels.

Snowflake Study Guide — Data Protection and Governance:Clarifies that setting retention to zero disables historical data storage and deactivates Time Travel functionality.

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.

In Snowflake, when a table or schema is dropped, it is not immediately deleted but retained for the configured data retention period, also known as "Time Travel." During this period, users can use commands like UNDROP to recover the dropped object if needed. After the retention period expires, the object is then moved to Fail-safe (if applicable) for an additional seven days before being permanently removed. This feature is intended to provide data protection and recovery options in case of accidental deletions.

In Snowflake, a role is an access control entity that can be created as part of a hierarchy within an account. Roles are used to grant and manage privileges in a structured and scalable manner.

Understanding Roles:

Roles are logical entities that group privileges together.

They are used to control access to securable objects like tables, views, warehouses, and more.

Role Hierarchy:

Roles can be organized into a hierarchy, allowing for the inheritance of privileges.

A role higher in the hierarchy (parent role) can grant its privileges to a lower role (child role), simplifying privilege management.

Creating Roles:

Roles can be created using the CREATE ROLE command.

You can define parent-child relationships by granting one role to another.

Example Usage:

CREATE ROLE role1;

CREATE ROLE role2;

GRANT ROLE role1 TO role2;

In this example,role2inherits the privileges ofrole1.

Benefits:

Simplifies privilege management: Hierarchies allow for efficient privilege assignment and inheritance.

Enhances security: Roles provide a clear structure for managing access control, ensuring that privileges are granted appropriately.

Snowflake Documentation: Access Control in Snowflake

Snowflake Documentation: Creating and Managing Roles

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.

In Snowflake, the use of streams impacts how the data retention period for a table is handled, particularly in scenarios where the stream has not been consumed. The key point to understand is that Snowflake's streams are designed to capture data manipulation language (DML) changes such as INSERTS, UPDATES, and DELETES that occur on asource table. Streams maintain a record of these changes until they are consumed by a DML operation or a COPY command that references the stream.

When a stream is created on a table and remains unconsumed, Snowflake extends the data retention period of the table to ensure that the changes captured by the stream are preserved. This extension is specifically up to the point in time represented by the stream's offset, which effectively ensures that the data necessary for consuming the stream's contents is retained. This mechanism is in place to prevent data loss and ensure the integrity of the stream's data, facilitating accurate and reliable data processing and analysis based on the captured DML changes.

This behavior emphasizes the importance of managing streams and their consumption appropriately to balance between data retention needs and storage costs. It's also crucial to understand how this temporary extension of the data retention period impacts the overall management of data within Snowflake, including aspects related to data lifecycle, storage cost implications, and the planning of data consumption strategies.

The search optimization service in Snowflake enhances query performance by maintaining a persistent data structure. This structure indexes the values of table columns across micro-partitions, allowing Snowflake to quickly identify which micro-partitions contain relevant data for a query. By efficiently narrowing down the search space, this service reduces the amount of data scanned during query execution, leading to faster response times and more efficient use of resources.

A Directory Table stores metadata about unstructured data files in a stage, such as size, last-modified timestamp, and file URL. This metadata helps query or track files without ingesting them into a table.

TheSearch Optimization Servicein Snowflake enhances query performance by creating apersistent data structurethat enables faster access to specific data, particularly for queries with selective filters oncolumns not often used in clustering. This persistent structure accelerates data retrieval without depending on clustering or caching, thereby improving response times for targeted queries.

Snowflake's micro-partitioning automatically manages table structure, but search optimization allows further enhancement for certain high-frequency, specific access patterns.