COF-C02 Free Practice Test

- (Topic 1)
What are the default Time Travel and Fail-safe retention periods for transient tables?

1. A. Time Travel - 1 da
2. B. Fail-safe - 1 day
3. C. Time Travel - 0 day
4. D. Fail-safe - 1 day
5. E. Time Travel - 1 da
6. F. Fail-safe - 0 days
7. G. Transient tables are retained in neither Fail-safe nor Time Travel

Correct Answer: C
Transient tables in Snowflake have a default Time Travel retention period of 1 day, which allows users to access historical data within the last 24 hours. However, transient tables do not have a Fail-safe period. Fail-safe is an additional layer of data protection that retains data beyond the Time Travel period for recovery purposes in case of extreme data loss. Since transient tables are designed for temporary or intermediate workloads with no requirement for long-term durability, they do not include a Fail-safe period by default1.
References:
✑ Snowflake Documentation on Storage Costs for Time Travel and Fail-safe

- (Topic 4)
What tasks can an account administrator perform in the Data Exchange? (Select TWO).

1. A. Add and remove members.
2. B. Delete data categories.
3. C. Approve and deny listing approval requests.
4. D. Transfer listing ownership.
5. E. Transfer ownership of a provider profile.

Correct Answer: AC
An account administrator in the Data Exchange can perform tasks such as adding and removing members and approving or denying listing approval requests. These tasks are part of managing the Data Exchange and ensuring that only authorized listings and members are part of it12.

- (Topic 1)
In the query profiler view for a query, which components represent areas that can be used to help optimize query performance? (Select TWO)

1. A. Bytes scanned
2. B. Bytes sent over the network
3. C. Number of partitions scanned
4. D. Percentage scanned from cache
5. E. External bytes scanned

Correct Answer: AC
In the query profiler view, the components that represent areas that can be used to help optimize query performance include ??Bytes scanned?? and ??Number of partitions scanned??. ??Bytes scanned?? indicates the total amount of data the query had to read and is a direct indicator of the query??s efficiency. Reducing the bytes scanned can lead to lower data transfer costs and faster query execution. ??Number of partitions scanned?? reflects how well the data is clustered; fewer partitions scanned typically means better performance because the system can skip irrelevant data more effectively.
References:
✑ [COF-C02] SnowPro Core Certification Exam Study Guide
✑ Snowflake Documentation on Query Profiling1

- (Topic 1)
How would you determine the size of the virtual warehouse used for a task?

1. A. Root task may be executed concurrently (i.
2. B. multiple instances), it is recommended to leave some margins in the execution window to avoid missing instances of execution
3. C. Querying(select)the size of the stream content would help determine the warehouse siz
4. D. For example, if querying large stream content, use a larger warehouse size
5. E. If using the stored procedure to execute multiple SQL statements, it's best to test run the stored procedure separately to size the compute resource first
6. F. Since task infrastructure is based on running the task body on schedule, it's recommended to configure the virtual warehouse for automatic concurrency handling using Multi-cluster warehouse (MCW) to match the task schedule

Correct Answer: D
The size of the virtual warehouse for a task can be configured to handle concurrency automatically using a Multi-cluster warehouse (MCW). This is because tasks are designed to run their body on a schedule, and MCW allows for scaling compute resources to match the task??s execution needs without manual intervention. References: [COF-C02] SnowPro Core Certification Exam Study Guide

- (Topic 5)
Which Snowflake layer is associated with virtual warehouses?

1. A. Cloud services
2. B. Query processing
3. C. Elastic memory
4. D. Database storage

Correct Answer: B
The layer of Snowflake's architecture associated with virtual warehouses is the Query Processing layer. Virtual warehouses in Snowflake are dedicated compute clusters that execute SQL queries against the stored data. This layer is responsible for the entire query execution process, including parsing, optimization, and the actual computation. It operates independently of the storage layer, enabling Snowflake to scale compute and storage resources separately for efficiency and cost-effectiveness.
References:
✑ Snowflake Documentation: Snowflake Architecture