- (Topic 5)
When floating-point number columns are unloaded to CSV or JSON files, Snowflake truncates the values to approximately what?
Correct Answer:D
When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files. References:
✑ Snowflake Documentation: Data Unloading Considerations
- (Topic 5)
Which Snowflake layer is associated with virtual warehouses?
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
- (Topic 3)
Which REST API can be used with unstructured data?
Correct Answer:C
The REST API used with unstructured data in Snowflake is GET /api/files/, which retrieves (downloads) a data file from an internal or external stage4.
- (Topic 1)
Which of the following are benefits of micro-partitioning? (Select TWO)
Correct Answer:BC
Micro-partitions in Snowflake are immutable objects, which means once they are written, they cannot be modified. This immutability supports the use of Time Travel, allowing users to access historical data within a defined period. Additionally, micro-partitions can significantly reduce the amount of I/O from object storage to virtual warehouses. This is because Snowflake??s query optimizer can skip over micro-partitions that do not contain relevant data for a query, thus reducing the amount of data that needs to be scanned and transferred.
References: [COF-C02] SnowPro Core Certification Exam Study Guide https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html
- (Topic 1)
What happens to the underlying table data when a CLUSTER BY clause is added to a Snowflake table?
Correct Answer:D
When a CLUSTER BY clause is added to a Snowflake table, it specifies one or more columns to organize the data within the table??s micro-partitions. This clustering aims to colocate data with similar values in the same or adjacent micro-partitions. By doing so, it enhances the efficiency of query pruning, where the Snowflake query optimizer can skip over irrelevant micro-partitions that do not contain the data relevant to the query, thereby improving performance.
References:
✑ Snowflake Documentation on Clustering Keys & Clustered Tables1.
✑ Community discussions on how source data??s ordering affects a table with a cluster key