When ASMM is disabled, the sizes of the automatically managed SGA components remain at their current values. ASMM is controlled by theSGA_TARGETparameter. IfSGA_TARGETis 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 settingSGA_TARGETto 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

Statspack is a performance diagnostic tool provided by Oracle prior to the introduction of the Automatic Workload Repository (AWR). At snap level 6, Statspack captures the following types of statistics:
* A (Correct):Parent and child latches are captured. Latch statistics provide information about contention for latches, which are low-level serialization mechanisms used by Oracle.
* E (Correct):Enqueue statistics, which provide information on the waits for locks that manage the concurrency between users.
* F (Correct):Segment-level statistics, which provide detailed information on database segments such as tables, indexes, etc., to identify I/O and contention issues.
* C (Incorrect):While optimizer execution plans are an essential aspect of performance tuning, detailed execution plan capture is not part of the Statspack report at level 6.
* D (Incorrect):Plan usage data refers to how frequently a plan is being used, which is more associated with AWR and not typically captured in Statspack reports.
References:
* Oracle Database Performance Tuning Guide:Using Statspack

In Oracle Database, theV$SESSION_LONGOPSview provides insights into various operations within the database that are expected to take more than six seconds to complete. These include operations related to RMAN (Recovery Manager), such as database duplication tasks. This view displays information about the progress of these long-running operations, including the start time, elapsed time, and estimated time to completion.
When an RMAN session is performing a database duplication and is observed to be slower than usual, checking theV$SESSION_LONGOPSview can give an approximation ofwhen the RMAN operation might complete. This view includes fields likeTIME_REMAININGandELAPSED_SECONDSthat help in estimating the completion time of the operation based on its current progress.
References:
* Oracle Database Reference:V$SESSION_LONGOPS
* Oracle Database Backup and Recovery User's Guide:Monitoring RMAN Jobs

The question asks to reduce database I/O impact without increasing the buffer cache size or modifying SQL statements. This indicates a need to reduce the physical I/O required to access the data. Let's analyze the scenario and the options.
Analysis of the AWR Report:
* Top Wait Events:
* The top foreground wait event is db file sequential read, which accounts for 40.4% of DB time.
This indicates significant physical I/O operations, primarily single-block reads, which are typically associated with index access.
* Reducing the physical I/O associated with db file sequential read can significantly improve performance.
* SQL Ordered by Reads:
* The SQL consuming the most reads involves high physical I/O. This confirms the need to reduce I
/O overhead by compressing data efficiently to minimize physical reads.
Compression Techniques and Their Suitability:
* A. COLUMN STORE COMPRESS FOR QUERY LOW:
* This option is a columnar compression method that optimizes for query performance but provides less compression compared to the HIGH option. While effective, it is not as suitable as FOR QUERY HIGH for reducing I/O.
* B. STORE COMPRESS:
* This is the basic compression option for tables and does not offer the advanced capabilities required for reducing significant physical I/O for queries.
* C. ROW STORE COMPRESS ADVANCED:
* This is a row-level compression that is suitable for OLTP workloads. While it reduces storage, it does not reduce query-related I/O as effectively as columnar compression.
* D. COLUMN STORE COMPRESS FOR QUERY HIGH (Correct Option):
* This is the most effective option for reducing query-related I/O. It:
* Uses columnar compression to reduce the size of data stored on disk.
* Reduces the number of physical reads by compressing data highly, meaning fewer blocks need to be read.
* Optimizes query performance for analytical workloads, which aligns with the scenario described in the AWR report.
Why COLUMN STORE COMPRESS FOR QUERY HIGH Is the Best Fit:
* It is designed to improve query performance by minimizing the amount of I/O required.
* Suitable for environments with heavy read operations (as indicated by the db file sequential read waits).
* Does not require changes to SQL or buffer cache size, adhering to the constraints in the question.
Reference to Oracle Documentation:
* Oracle Database 19c Performance Tuning Guide:
* Section: Using Compression to Reduce Storage and I/O Requirements.
* Discussion of columnar compression techniques for reducing I/O in query-intensive environments.
* Oracle Advanced Compression Documentation:
* Details on COLUMN STORE COMPRESS FOR QUERY HIGH and its benefits for analytical workloads.

The AWR excerpt points to excessive physical reads on the SALES table and index, suggesting the need for optimizing table storage and access.
Partitioning the SALES table (A) can reduce 'db file sequential read' waits by breaking down the large SALES table into smaller, more manageable pieces. This can localize the data and reduce the I/O necessary for query operations.
Compressing the SALES table (D) can also help reduce I/O by minimizing the amount of data that needs to be read from disk. This can also improve cache utilization and reduce the 'db file sequential read' waits.
References:
* Oracle Database VLDB and Partitioning Guide, 19c
* Oracle Database Administrator's Guide, 19c
These changes are recommended based on Oracle's best practices for managing large tables and reducing I/O waits, ensuring better performance and efficiency.