Snowflake ADA-C01 Exam With Confidence Using Practice Dumps

According to the Using Persisted Query Results documentation, the RESULT_SCAN function allows you to query the result set of a previous command as if it were a table. This can improve the performance of the second query by avoiding reading the same data from micro-partitions again. The other actions do not provide optimal performance for the second query because:

•Assigning additional clusters to the virtual warehouse does not affect the data access speed, but only the query execution speed. It also increases the cost of the warehouse.

•Increasing the STATEMENT_TIMEOUT_IN_SECONDS parameter in the session does not improve the performance of the query, but only allows it to run longer before timing out. It also increases the risk of resource contention and deadlock.

•Preventing the virtual warehouse from suspending between the running of the first and second queries does not guarantee that the data will be cached in memory, as Snowflake uses a least recently used (LRU) cache eviction policy. It also increases the cost of the warehouse.

According to the Snowflake Warehouse Cost Optimization blog post, one of the strategies to reduce the cost of running a warehouse is to use a multi-cluster warehouse with auto-scaling enabled. This allows the warehouse to automatically adjust the number of clusters based on the concurrency demand and the queue size. A multi-cluster warehouse can also be configured with a minimum and maximum number of clusters, as well as a scaling policy to control the scaling behavior. This way, the warehouse can handle the parallel load queries efficiently without wasting resources or credits. The blog post also suggests using a smaller warehouse size, such as SMALL or XSMALL, for loading data, as it can perform better than a larger warehouse size for small INSERTs. Therefore, the best option to reduce the costs while minimizing the overall load times for migrating data warehouse history is to keep the warehouse as a SMALL or XSMALL and configure it as a multi-cluster warehouse to handle the parallel load queries. The other options are incorrect because:

•A. Deploying another 2XL warehouse to handle a portion of the load queries will not reduce the costs, but increase them. It will also introduce complexity and potential inconsistency in managing the data loading process across multiple warehouses.

•B. Changing the 2XL warehouse to 4XL will not reduce the costs, but increase them. It will also provide more compute resources than needed for small INSERTs, which are not CPU-intensive but I/O-intensive.

•D. Converting the INSERTs to several tables will not reduce the costs, but increase them. It will also create unnecessary data duplication and fragmentation, which will affect the query performance and data quality.

The inclusion of the CURRENT_TIMESTAMP() function in the second query prevents it from re-using the first query’s persisted result cache because this function makes each execution unique due to the constantly changing timestamp. According to the Snowflake documentation, “The query does not include non-reusable functions, which return different results for successive runs of the same query. UUID_STRING, RANDOM, and RANDSTR are good examples of non-reusable functions.” The CURRENT_TIMESTAMP() function is another example of a non-reusable function, as it returns the current date and time at the start of query execution, which varies for each run. Therefore, the second query is not identical to the first query, and the result cache is not reused. The other options are either incorrect or irrelevant to the question. Option B is incorrect, as the CURRENT_DATE() function is a reusable function, as it returns the same value for all queries executed within the same day. Option C is irrelevant, as the virtual warehouse used to execute the query does not affect the result cache reuse. Option D is also irrelevant, as the role used to execute the query does not affect the result cache reuse, as long as the role has the necessary access privileges for all the tables used in the query.