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What is a key consideration when setting up search optimization service for a table?
A. Search optimization service works best with a column that has a minimum of 100 K distinct values.
B. Search optimization service can help to optimize storage usage by compressing the data into a GZIP format.
C. Search optimization service can significantly improve query performance on partitioned external tables.
D. The table must be clustered with a key having multiple columns for effective search optimization.
Answer: A
Explanation:
A: The Search Optimization Service is designed to accelerate the performance of queries that use filters on large tables. One of the key considerations for its effectiveness is using it with tables where the columns used in the filter conditions have a high number of distinct values, typically in the hundreds of thousands or more. This is because the service creates a map-reduce-like index on the column to speed up queries that use point lookups or range scans on that column. The more unique values there are, the more effective the index is at narrowing down the search space.
Reference: Snowflake documentation and best practices on the Search Optimization Service, which would be covered under the SnowPro Advanced: Architect certification materials.
NEW QUESTION # 121
You have a table named JSON_TBL which has a variant column JSON_VAR. The json stored in that table looks as below
{
"COURSE_DESC": "SNOWFLAKE CERTIFICATION",
"COURSE_ID": 1000,
"DURATION": 2
}
if you run a query SELECT JSON_VAR:Course_id FROM JSON_TBL; what will it return
A. 0
B. NULL
C. 1
Answer: B
NEW QUESTION # 122
An Architect is troubleshooting a query with poor performance using the QUERY function. The Architect observes that the COMPILATION_TIME Is greater than the EXECUTION_TIME.
What is the reason for this?
A. The query Is reading from remote storage
B. The query Is queued for execution.
C. The query has overly complex logic.
D. The query is processing a very large dataset.
Answer: C
Explanation:
* The correct answer is B because the compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. The compilation time depends on the complexity of the query, such as the number of tables, columns, joins, filters, aggregations, subqueries, etc. The more complex the query, the longer it takes to compile.
* Option A is incorrect because the query processing time is not affected by the size of the dataset, but by the size of the virtual warehouse. Snowflake automatically scales the compute resources to match the data volume and parallelizes the query execution. The size of the dataset may affect the execution time, but not the compilation time.
* Option C is incorrect because the query queue time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query waits for a warehouse slot before it starts
* running. The query queue time depends on the warehouse load, concurrency, and priority settings.
* Option D is incorrect because the query remote IO time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query spends reading data from remote storage, such as S3 or Azure Blob Storage. The query remote IO time depends on the network latency, bandwidth, and caching efficiency. References:
* Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time: This article explains why the total duration (compilation + execution) time is an essential metric to measure query performance in Snowflake. It discusses the reasons for the long compilation time, including query complexity and the number of tables and columns.
* Exploring Execution Times: This document explains how to examine the past performance of queries and tasks using Snowsight or by writing queries against views in the ACCOUNT_USAGE schema. It also describes the different metrics and dimensions that affect query performance, such as duration, compilation, execution, queue, and remote IO time.
* What is the "compilation time" and how to optimize it?: This community post provides some tips and best practices on how to reduce the compilation time, such as simplifying the query logic, using views or common table expressions, and avoiding unnecessary columns or joins.
NEW QUESTION # 123
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:
What would be the output of this query?
A. Table T_SALES_CLONE successfully created.
B. The offset -> is not a valid clause in the clone operation.
C. Time Travel data is not available for table T_SALES.
D. Syntax error line 1 at position 58 unexpected 'at'.
Answer: A
Explanation:
The query is executing a clone operation on an existing table t_sales with an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of the at(offset => -60*30) clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the table t_sales exists and has been modified within the last 30 minutes, and considering the data_retention_time_in_days is set to 1 day (which enables time travel queries for the past
24 hours), the table t_sales_clone would be successfully created based on the state of t_sales 30 minutes before the clone command was issued.
NEW QUESTION # 124
You ran the below query. I have a warehouse with auto suspend set at 5 seconds
SELECT * FROM INVENTORY;
The query profile looks like as below. Please see below 'Percentage scanned from cache' is 0%
You ran the query again before 5 seconds has elapsed and the query profile looks as below. Look at the 'Percentage scanned for cache', it is 75%
You ran the query again after 5 seconds. The query profile looks as below. Look at the 'Percentage scanned from cache', it is zero again.
Why is this happening?
A. The third run of the query used query result cache
B. The second run of the query used data cache to retrieve part of the result since it ran before the warehouse was suspended
C. The second run of the query used query result cache
Answer: B
NEW QUESTION # 125
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