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Which two statements are true about cursor sharing?
A) When Cursor_sharing is set to FORCE, Oracle tries to avoid hard parses by replacing literals in SQL statements with bind variables, even if the original statement didn't include bind variables. This can lead to the use of a single execution plan for multiple executions of a statement with different literal values, which might not be optimal for all executions.
D) Setting cursor_sharing to EXACT ensures that SQL statements must match exactly for them to share a cursor. This setting prevents the use of Adaptive Cursor Sharing (ACS) since ACS relies on the ability to share cursors among similar statements that differ only in their literal values. With EXACT, there's no cursor sharing for statements with different literals, hence no opportunity for ACS to operate.
Oracle Database SQL Tuning Guide, 19c
Oracle Database Reference, 19c
Which two statements are true about Data Pump import for objects that used the in Memory (IM) column store in their source database?
When importing objects that used the In-Memory (IM) column store in their source database using Oracle Data Pump, the following statements are true:
D (Correct): The TRANSFORM clause can be used to alter object creation DDL during import operations. This can include adding the INMEMORY clause to tables that were not originally using the IM column store.
F (Correct): The import operation can preserve the INMEMORY attributes of tables as they were at the time of export, effectively replicating the IM column store settings from the source database.
The other statements are not accurate in the context of Data Pump import:
A (Incorrect): Data Pump does not give preference to the IM column store clauses at the tablespace level over table-level definitions unless explicitly specified by the TRANSFORM clause.
B (Incorrect): While Data Pump can transport existing INMEMORY attributes, it is not mandatory. It is controlled by the INCLUDE or EXCLUDE Data Pump parameters or the TRANSFORM clause.
C (Incorrect): The INMEMORY_CLAUSE parameter is not part of the Data Pump Export utility. To modify the IM column store clauses, you would use the TRANSFORM parameter during import, not export.
E (Incorrect): Data Pump does not ignore the IM column store clause unless specifically instructed to do so via the EXCLUDE parameter.
Which two types of performance problems are reported by ADDM for PDBS?
The Automatic Database Diagnostic Monitor (ADDM) analyzes and reports on various types of performance problems. For Pluggable Databases (PDBs), it can identify issues such as I/O capacity limits which may hinder the overall performance by causing bottlenecks. Additionally, ADDM can report on user I/O waits, which can indicate performance issues related to the time it takes for user queries to read data from the disk.
Oracle Multitenant Administrator's Guide, 19c
Oracle Database Performance Tuning Guide, 19c
You are informed that the RMAN session that is performing the database duplication is much slower than usual. You want to know the approximate time when the rman operation will be completed.
Which view has this information?
In Oracle Database, the V$SESSION_LONGOPS view provides insights into various operations within the database that are expected to take more than six seconds to complete. These include operations related to RMAN (Recovery Manager), such as database duplication tasks. This view displays information about the progress of these long-running operations, including the start time, elapsed time, and estimated time to completion.
When an RMAN session is performing a database duplication and is observed to be slower than usual, checking the V$SESSION_LONGOPS view can give an approximation of when the RMAN operation might complete. This view includes fields like TIME_REMAINING and ELAPSED_SECONDS that help in estimating the completion time of the operation based on its current progress.
Oracle Database Reference: V$SESSION_LONGOPS
Oracle Database Backup and Recovery User's Guide: Monitoring RMAN Jobs
You manage a 19c database with default optimizer settings.
This statement is used extensively as subquery in the application queries:
SELECT city_id FROM sh2.sales WHERE city_id=:Bl
You notice the performance of these queries is often poor and, therefore, execute:
SELECT city_id,COUNT(*) FROM sh2.sales GROUP BY city_id;
Examine the results:
There is no index on the CITY_ID column.
Which two options improve the performance?
In this scenario, creating an index and generating frequency histograms are two methods that can potentially improve performance:
A (Correct): Generating frequency histograms on the CITY_ID column can help the optimizer make better decisions regarding the execution plan, especially if the data distribution is skewed. Histograms provide the optimizer with more detailed information about the data distribution in a column, which is particularly useful for columns with non-uniform distributions.
B (Correct): Creating an index on the CITY_ID column would speed up queries that filter on this column, especially if it's used frequently in the WHERE clause as a filter. An index would allow for an index range scan instead of a full table scan, reducing the I/O and time needed to execute such queries.
C (Incorrect): While SQL profiles can be used to improve the performance of specific SQL statements, they are usually not the first choice for such a problem, and creating a profile does not replace the need for proper indexing or statistics.
D (Incorrect): Forcing the subquery to use dynamic sampling might not provide a consistent performance benefit, especially if the table statistics are not representative or are outdated. However, dynamic sampling is not as effective as having accurate statistics and a well-chosen index.
E (Incorrect): Adaptive plans can adjust the execution strategy based on the conditions at runtime. While they can be useful in certain scenarios, in this case, creating an index and ensuring accurate statistics would likely provide a more significant performance improvement.
Oracle Database SQL Tuning Guide: Managing Optimizer Statistics
Oracle Database SQL Tuning Guide: Using Indexes and Clusters
