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NEW QUESTION # 226
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?
Answer: A
Explanation:
A multi-cluster warehouse is a virtual warehouse that can scale compute resources by adding or removing clusters based on the workload demand. A multi-cluster warehouse can improve the performance of a particular workload by reducing the query queue time and the data spillage to local storage. To identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse, you need to analyze the query history and look for the following indicators:
* High average queued load: This metric shows the average number of queries waiting in the queue for each warehouse cluster. A high value indicates that the warehouse is overloaded and cannot handle the concurrency demand.
* High bytes spilled to local storage: This metric shows the amount of data that was spilled from memory to local disk during query processing. A high value indicates that the warehouse size is too small and cannot fit the data in memory.
* High variation in workload: This metric shows the fluctuation in the number of queries submitted to the warehouse over time. A high variation indicates that the workload is unpredictable and dynamic, and requires a flexible scaling policy.
The query in option C is the best one to identify these indicators, as it selects the date, warehouse name, bytes spilled to local storage, and sum of average queued load from the query history table, and filters the results where bytes spilled to local storage is greater than zero. This query will show the days and warehouses that experienced data spillage and high queue time, and could benefit from a multi-cluster warehouse with auto-scale mode.
The query in option A is not correct, as it only selects the date and warehouse name, and does not include any metrics to measure the performance of the workload. The query in option B is not correct, as it selects the date, warehouse name, and average execution time, which is not a good indicator of the need for a multi-cluster warehouse. The query in option D is not correct, as it selects the date, warehouse name, and average credits used, which is not a good indicator of the need for a multi-cluster warehouse either.
References: Multi-cluster Warehouses, Query History View, Reducing Queues
NEW QUESTION # 227
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)
Answer: D,E
Explanation:
The built-in Snowflake features that make use of the change tracking metadata for a table are the CHANGES clause and a STREAM object. The CHANGES clause enables querying the change tracking metadata for a table or view within a specified interval of time without having to create a stream with an explicit transactional offset1. A STREAM object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC)2. The other options are incorrect because they do not make use of the change tracking metadata for a table. The MERGE command performs insert, update, or delete operations on a target table based on the results of a join with a source table3. The UPSERT command is not a valid Snowflake command. The CHANGE_DATA_CAPTURE command is not a valid Snowflake command. References: CHANGES | Snowflake Documentation, Change Tracking Using Table Streams | Snowflake Documentation, MERGE | Snowflake Documentation
NEW QUESTION # 228
Data sharing is supported only between provider and consumer accounts in same region
Answer: B
NEW QUESTION # 229
The following DDL command was used to create a task based on a stream:
Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?
Answer: A
Explanation:
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. Reference:
[CREATE TASK | Snowflake Documentation]
[Using Streams and Tasks | Snowflake Documentation]
[CREATE WAREHOUSE | Snowflake Documentation]
NEW QUESTION # 230
You have a need to make external file data available to your users with the lowest latency. The files are on an external stage in AWS.
What feature of Snowflake is the most appropriate to use
Answer: B
NEW QUESTION # 231
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