ARA-C01 Exam Dumps : SnowPro Advanced: Architect Certification Exam

The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.

The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.

The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.

Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.

Option C is incorrect because it uses identifier, which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.

Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead. References:

Snowflake Documentation: Identifier Function

Snowflake Documentation: Session Variables

Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide

The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory

 Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake’s micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. References:

Snowflake Connector for Kafka

Snowpipe

Snowflake Streams

Snowflake Spark Connector