SKILL BENCHMARK
Snowflake Performance Monitoring Competency (Intermediate Level)
- 20m
- 20 questions
The Snowflake Performance Monitoring Competency (Intermediate Level) benchmark will measure your ability to create and configure warehouses and multi-cluster warehouses to improve query performance and concurrency, as well as evaluate query result caching, local disk caching, and the query acceleration service (QAS) on Snowflake. You will also be assessed on your skills in implementing, contrasting, and performing benchmark clustering and identifying usage scenarios for views. A learner who scores high on this benchmark demonstrates that they have advanced Snowflake competency and can monitor and optimize Snowflake performance with minimal supervision.
Topics covered
- analyze how databases, schemas, tables, and partitions figure into Snowflake's data model
- analyze the impact of query result caching and local disk caching
- analyze the results of an eligible query run with and without query acceleration
- analyze the working of the query acceleration service (QAS) and caching on Snowflake
- choose between resizing warehouses and multi-cluster warehouses
- choose the clustering key appropriately
- configure a multi-cluster warehouse in auto-scale mode with an economy scaling policy
- create and query views
- create a new compute warehouse and explore warehouse settings
- evaluate choices of clustering keys and pick the most suitable
- evaluate the benefits of large compute warehouses for more complex queries and greater concurrency
- instantiate a warehouse with the QAS turned on and evaluate a query for eligibility
- load data into a table for benchmarking purposes
- outline scenarios where query result caching is and is not used
- outline the defining aspects of Snowflake and analyze the three layers in Snowflake architecture
- outline the working of clustering, time travel, and fail-safe
- populate a table with data using PUT and COPY INTO and use TABLESAMPLE to shuffle the data in that table
- provide an overview of the intuition behind clustering depth and the number of overlapping partitions
- provide an overview of the properties and use cases of views, materialized views, and secure views
- use views and role-based access control to grant different users access to different subsets of a table
