Snowflake Updated DEA-C01 Exam Questions and Answers by ettie

The recommendations that apply to this scenario are:

• Ensure that data files are 100-250 MB (or larger) in size compressed: This recommendation will improve Snowpipe performance by reducing the number of files that need to be loaded and increasing the parallelism of loading. Smallerfiles can cause performance degradation or errors due to excessive metadata operations or network latency.
• Verify each value of each unique element stores a single native data type (string or number): This recommendation will improve Snowpipe performance by avoiding data type conversions or errors when loading JSON data into variant columns. Snowflake supports two native data types for JSON elements: string and number. If an element has mixed data types across different files or records, such as string and boolean, Snowflake will either convert them to string or raise an error, depending on the FILE_FORMAT option.
• Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute: This recommendation will minimize Snowpipe costs by reducing the number of notifications that need to be sent to Snowpipe for auto-ingestion. Snowpipe charges for notifications based on the number of files per notification and the frequency of notifications. By creating smaller files and staging them at a lower frequency, fewer notifications will be needed.

This option is the best way to ensure that Snowpipe is performant while minimizing costs. By splitting the files before loading them, the Data Engineer can reduce the size of each file and increase the parallelism of loading. By setting the SIZE_LIMIT option to 250 MB, the Data Engineer can specify the maximum file size that can be loaded bySnowpipe, which can prevent performance degradation or errors due to large files. The other options are not optimal because:

• Increasing the size of the virtual warehouse used by Snowpipe will increase the performance but also increase the costs, as larger warehouses consume more credits per hour.
• Changing the file compression size and increasing the frequency of the Snowpipe loads will not have much impact on performance or costs, as Snowpipe already supports various compression formats and automatically loads files as soon as they are detected in the stage.
• Decreasing the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose will not affect Snowpipe performance or costs, as Snowpipe does not depend on Kinesis Firehose buffer size but rather on its own SIZE_LIMIT option.

 The query that will provide information about the micro-partition layout for a table named invoice using a built-in function is SELECT SYSTEM$CLUSTERING_INFORMATION(‘Invoice’);. The SYSTEM$CLUSTERING_INFORMATION function returns information about the clustering status of a table, such as the clustering key, the clustering depth, the clustering ratio, the partition count, etc. The function takes one argument: the table name in a qualified or unqualified form. In this case, the table name is Invoice and it is unqualified, which means that it will use the current database and schema as the context. The other options are incorrect because they do not use a valid built-in function for providing information about the micro-partition layout for a table. Option B is incorrect because it uses $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which is not a valid function name. Option C is incorrect because it uses CALL instead of SELECT, which is not a valid way to invoke a table function. Option D is incorrect because it uses CALL instead of SELECT and $CLUSTERING_INFORMATION instead of SYSTEM$CLUSTERING_INFORMATION, which are both invalid.

Creating a DataFrame from a table will start a virtual warehouse because it requires reading data from Snowflake. The other options will not start a virtual warehouse because they either operate on local data or use an existing session to query Snowflake.