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Oracle Database 19c Performance and Tuning Management Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which two statements are true about disabling Automatic Shared Memory Management (ASMM)?

A. All SGA components excluding fixed SGA and other internal allocations are readjusted immediately after disabling ASMM.
B. All SGA components retain their current sizes at the time of disabling.
C. The SGA size remains unaffected after disabling ASMM.
D. Both SGA_TARGET and SGA_MAX_SIZE must be set to zero.
E. All auto-tuned SGA components are reset to their original user-defined values.
F. It requires a database instance restart to take effect.

Answer: B,C

Explanation:
When ASMM is disabled, the sizes of the automatically managed SGA components remain at their current values. ASMM is controlled by the SGA_TARGET parameter. If SGA_TARGET is set to a non-zero value, ASMM is enabled and Oracle will automatically manage the sizes of the various SGA components. When ASMM is disabled, by setting SGA_TARGET to zero, the SGA components that were automatically sized will retain their current sizes rather than being reset to their original user-defined values. The overall size of the SGA remains the same unless manually changed by modifying individual component sizes or SGA_MAX_SIZE.
References:
* Oracle Database Administration Guide, 19c
* Oracle Database Performance Tuning Guide, 19c

NEW QUESTION # 25
The CURS0R_SHARING and OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES parameters are set to default. The top five wait events in an awr report are due to a large number of hard parses because of several almost identical SQL statements.
Which two actions could reduce the number of hard parses?

A. Set the CURSOR_SHARING parameter to FORCE.
B. Create the RECYCLE cache and cache tables accessed by the SQL statements.
C. Set OPTIMIZER_CAPTURE_SQL_PLAN_BASELINESto TRUE.
D. Create the KEEP cache and cache tables accessed by the SQL statements.
E. Increase the size of the library cache.

Answer: A,E

Explanation:
To reduce the number of hard parses due to several almost identical SQL statements, you can take the following actions:
* C (Correct):Increasing the size of the library cache can help reduce hard parses by providing more
* memory to store more execution plans. This allows SQL statements to be shared more effectively.
* E (Correct):Setting theCURSOR_SHARINGparameter toFORCEwill cause Oracle to replace literals in SQL statements with bind variables, which can significantly reduce the number of hard parses by making it more likely that similar SQL statements will share the same execution plan.
The other options do not directly impact the number of hard parses:
* A (Incorrect):Creating the KEEP cache and caching tables accessed by the SQL statements can improve performance for those tables, but it does not directly reduce the number of hard parses.
* B (Incorrect):Creating the RECYCLE cache and caching tables accessed by the SQL statements can make it more likely that objects will be removed from the cache quickly, which does not help with hard parse issues.
* D (Incorrect):SettingOPTIMIZER_CAPTURE_SQL_PLAN_BASELINEStoTRUEcan help stabilize SQL execution plans but will not reduce the number of hard parses. This parameter is used to automatically capture SQL plan baselines for repeatable SQL statements, which can prevent performance regressions due to plan changes.
References:
* Oracle Database Performance Tuning Guide:Minimizing Hard Parses
* Oracle Database SQL Tuning Guide:CURSOR_SHARING

NEW QUESTION # 26
Which three statements are true about using the in Memory (IM) column store?

A. It can improve OLTP workload performance by avoiding the use of indexes.
B. It does not improve performance for queries using cached results of function evaluations on columns from the same table.
C. It does not improve performance for queries that use join groups on columns from different tables.
D. It improves performance for queries joining several tables using bloom filter joins.
E. It does not improve performance for queries using user-defined virtual column results.
F. It does not require all database data to fit in memory to improve query performance.

Answer: A,D,F

Explanation:
The Oracle In-Memory (IM) column store feature enhances the performance of databases by providing a fast columnar storage format for analytical workloads while also potentially benefiting OLTP workloads.
* C (True):It can improve OLTP workload performance by providing a faster access path for full table scans and reducing the need for indexes in certain scenarios, as the In-Memory store allows for efficient in-memory scans.
* E (True):The In-Memory column store does not require all database data to fit in memory. It can be used selectively for performance-critical tables or partitions, and Oracle Database will manage the population and eviction of data as needed.
* F (True):In-Memory column store can significantly improve performance for queries joining several tables, especially when bloom filters are used, as they are highly efficient with the columnar format for large scans and join processing.
The other options provided are not correct in the context of the In-Memory column store:
* A (False):While In-Memory column store is designed for analytical queries rather than caching results of function evaluations, it does not specifically avoid improving performance for queries using cached results of function evaluations.
* B (False):In-Memory column store can improve the performance of queries that use join groups, which can be used to optimize joins on columns from different tables.
* D (False):In-Memory column store can improve the performance of queries using expressions, including user-defined virtual columns, because it supports expression statistics which help in
* optimizing such queries.
References:
* Oracle Database In-Memory Guide:In-Memory Column Store in Oracle Database
* Oracle Database In-Memory Guide:In-Memory Joins
* Oracle Database In-Memory Guide:In-Memory Aggregation

NEW QUESTION # 27
Multiple sessions are inserting data concurrently into a table that has an LOB column.
At some point in time, one of the sessions cannot find available space in the LOB segment and needs to allocate a new extent.
Which wait event will be raised in the other sessions that need space in the LOB column?

A. enq: TX - allocate ITL entry
B. enq: SQ - contention
C. enq: TM - contention
D. enq: HW - contention

Answer: D

Explanation:
When sessions concurrently insert data into a table with an LOB column and one session needs to allocate a new extent because it cannot find available space, the wait event associated with this contention is "enq: HW - contention". The HW stands for High Water Mark which is related to space allocation in the database segment. When a session needs to allocate a new extent, it may raise this wait event in other sessions that are also attempting to allocate space in the same LOB segment.
References
* Oracle Database 19c Reference Guide - enq: HW - contention

NEW QUESTION # 28
Database performance has degraded recently.
index range scan operations on index ix_sales_time_id are slower due to an increase in buffer gets on sales table blocks.
Examine these attributes displayed by querying DBA_TABLES:

Now, examine these attributes displayed by querying DBA_INDEXES:

Which action will reduce the excessive buffer gets?

A. Re-create the SALES table using the columns in IX_SALES_TIME_ID as the hash partitioning key.
B. Partition index IX_SALES_TIME_ID using hash partitioning.
C. Re-create the SALES table sorted in order of index IX_SALES_TIME_ID.
D. Re-create index IX_SALES_TIME_ID using ADVANCED COMPRESSION.

Answer: D

Explanation:
Given that index range scan operations on IX_SALES_TIME_ID are slower due to an increase in buffer gets, the aim is to improve the efficiency of the index access. In this scenario:
* B (Correct): Re-creating the index using ADVANCED COMPRESSION can reduce the size of the index, which can lead to fewer physical reads (reduced I/O) and buffer gets when the index is accessed, as more of the index can fit into memory.
The other options would not be appropriate because:
* A (Incorrect): Re-creating the SALES table sorted in order of the index might not address the issue of excessive buffer gets. Sorting the table would not improve the efficiency of the index itself.
* C (Incorrect): Using the columns in IX_SALES_TIME_ID as a hash partitioning key for the SALES table is more relevant to data distribution and does not necessarily improve index scan performance.
* D (Incorrect): Hash partitioning the index is generally used to improve the scan performance in a parallel query environment, but it may not reduce the number of buffer gets in a single-threaded query environment.
References:
* Oracle Database SQL Tuning Guide: Managing Indexes
* Oracle Database SQL Tuning Guide: Index Compression

NEW QUESTION # 29
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