traditionally analyzed the distribution of values withi…

You want to capture column group usage and gather extended statistics for better cardinality estimates for the customers table in the SH schema.
Examine the following steps:
1. Issue the SELECTDBMS_STATS. CREATE_EXTENDED_STATS(‘SH’, ‘CUSTOMERS’)from dual statement.
2.Execute the dbms_stats.seed_col_usage (null,‘SH’,500) procedure.
3.Execute the required queries on the customers table.
4.Issue the select dbms_stats.reportwcol_usage(‘SH’, ‘customers’) from dual statement.
Identify the correct sequence of steps. queries to ensure column group information is recorded for these queries. monitoring window. You simply have to call the DBMS_STATS.CREATE_EXTENDED_STATS function for each table.This function requires just two arguments, the schema name and the table name. From then on, statistics will be maintained for each column group whenever statistics are gathered on the table. object. traditionally analyzed the distribution of values within a column, he does not collect value-based relationships between columns.

You want to capture column group usage and gather extended statistics for better cardinality estimates for the customers table in the SH schema.
Examine the following steps:
1. Issue the SELECTDBMS_STATS. CREATE_EXTENDED_STATS(‘SH’, ‘CUSTOMERS’)from dual statement.
2.Execute the dbms_stats.seed_col_usage (null,‘SH’,500) procedure.
3.Execute the required queries on the customers table.
4.Issue the select dbms_stats.reportwcol_usage(‘SH’, ‘customers’) from dual statement.
Identify the correct sequence of steps. queries to ensure column group information is recorded for these queries. monitoring window. You simply have to call the DBMS_STATS.CREATE_EXTENDED_STATS function for each table.This function requires just two arguments, the schema name and the table name. From then on, statistics will be maintained for each column group whenever statistics are gathered on the table. object. traditionally analyzed the distribution of values within a column, he does not collect value-based relationships between columns.

A.
3, 2, 1, 4

B.
2, 3, 4, 1

C.
4, 1, 3, 2

D.
3, 2, 4, 1

Explanation:
Step 1 (2). Seed column usage
Oracle must observe a representative workload, in order to determine the appropriate column groups. Using the new procedure
DBMS_STATS.SEED_COL_USAGE, you tell Oracle how long it should observe the workload.
Step 2: (3) You don’t need to execute all of the queries in your work during this window. You can simply run explain plan for some of your longer running
Step 3. (1) Create the column groups
At this point you can get Oracle to automatically create the column groups for each of the tables based on the usage information captured during the
Note:
* DBMS_STATS.REPORT_COL_USAGE reports column usage information and records all the SQL operations the database has processed for a given
* The Oracle SQL optimizer has always been ignorant of the implied relationships between data columns within the same table. While the optimizer has
* Creating extended statistics
Here are the steps to create extended statistics for related table columns withdbms_stats.created_extended_stats:
1 – The first step is to create column histograms for the related columns.
2 – Next, we run dbms_stats.create_extended_stats to relate the columns together.
Unlike a traditional procedure that is invoked via an execute (“exec”) statement, Oracle extended statistics are created via a select statement.



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