Snowflake Updated COF-C02 Exam Questions and Answers by arlo-james

The Query Profile in Snowflake provides detailed insights into the execution of a query. It helps in troubleshooting performance issues by showing the steps of the query execution and the resources consumed. One of the key aspects it reveals is whether the virtual warehouse memory was sufficient for the query.

Access Query Profile: Navigate to the Query History page and select the query you want to analyze.

Examine Query Execution Steps: The Query Profile displays the different stages of the query execution, including the time taken and resources used at each step.

Identify Memory Issues: Look for indicators of memory issues, such as spilling to disk or memory errors, which suggest that the virtual warehouse memory might be too small.

References:

Snowflake Documentation: Query Profile

Snowflake Documentation: Optimizing Queries

In Snowflake, the consumption of credits by a virtual warehouse is determined by its size and the duration for which it runs. A size 3X-Large virtual warehouse consumes 128 credits if it runs continuously for 2 hours. This consumption rate is based on the principle that larger warehouses, capable of providing greater computational resources and throughput, consume more credits per hour of operation. The specific rate of consumption is defined by Snowflake’s pricing model and the scale of the virtual warehouse.References: Snowflake Pricing Documentation

Snowflake recommends considering adding a clustering key to a table when its size reaches 1 Terabyte (TB) or larger. Clustering keys help optimize the storage and query performance by organizing the data in a table based on the specified columns. This is particularly beneficial for large tables where data retrieval can become inefficient without proper clustering.

Why Clustering Keys Are Important: Clustering keys ensure that data stored in Snowflake is physically ordered in a way that aligns with the most frequent access patterns, thereby reducing the amount of scanned data during queries and improving performance.

Recommendation Basis: The recommendation for tables of size 1 TB or larger is based on the observation that smaller tables generally do not benefit as much from clustering, given Snowflake's architecture. However, as tables grow in size, the benefits of clustering become more pronounced.

Implementing Clustering Keys:

To set a clustering key for a table, you can use the CLUSTER BY clause during table creation or alter an existing table to add it:

CREATE TABLE my_table (... ) CLUSTER BY (column1, column2);

Or for an existing table:

ALTER TABLE my_table CLUSTER BY (column1, column2);

Snowflake's Continuous Data Protection (CDP) features encompass several mechanisms designed to protect data and ensure its availability and recoverability. Among these features, the one that has associated costs is Fail-safe.

Fail-safe is an additional layer of protection that kicks in after the Time Travel period expires. While Time Travel allows users to access historical data within a defined retention period (which can vary from 1 to 90 days depending on the Snowflake edition), Fail-safe provides a further 7 days (for a total of 7 additional days beyond the Time Travel period) during which Snowflake retains the data. This period is primarily intended for Snowflake's internal operations to recover data in the event of extreme scenarios, such as significant operational failures, and is not directly accessible by customers for data recovery purposes.

The associated costs with Fail-safe arise because Snowflake continues to store the data beyond the customer-specified Time Travel period, thereby incurring additional storage costs. It's important to note that while users do not incur direct costs for enabling Fail-safe (as it is an automatic feature of Snowflake), the extended storage of data during this period contributes to overall storage costs.

References:

Snowflake Documentation on Continuous Data Protection: Continuous Data Protection (CDP)

Snowflake Documentation on Fail-safe: Understanding Fail-safe