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

Server-generated alerts in Oracle Database are designed to notify DBAs and other administrators about issues within the database environment. These alerts can be triggered by a variety of conditions, including threshold-based metrics and specific events such as ORA- error messages. Here's how these options align with the statements provided:
* A (True):Server-generated alerts are indeed notifications from the Oracle Database Server that highlight existing or impending issues. These alerts are part of Oracle's proactive management capabilities, designed to inform administrators about potential problems before they escalate.
* C (True):These alerts are logged in the alert log of the Oracle Database. The alert log is a crucial diagnostic tool that records major events and changes in the database, including server-generated alerts.
This log is often the first place DBAs look when troubleshooting database issues.
* F (True):Server-generated alerts may include suggestions for correcting identified problems. Oracle Database often provides actionable advice within these alerts to assist in resolving issues more efficiently. These suggestions can range from adjusting configuration parameters to performing specific maintenance tasks.
Options B, D, and E do not accurately describe server-generated alerts:
* B (False):While the statement might have been true in some contexts, Oracle's server-generated alerts often include corrective suggestions, making this statement incorrect.
* D (False):Server-generated alerts can be viewed from various interfaces, not just the Cloud Control Database home page. They are accessible through Enterprise Manager, SQL Developer, and directly within the database alert log, among other tools.
* E (False):While it's true that threshold settings for some alerts can be modified, the method specified, usingDBMS_SERVER_ALERT, is not correct. Threshold settings are typically adjusted through Enterprise Manager or by modifying specific initialization parameters directly.
References:
* Oracle Database Documentation:Oracle Database 19c: Performance Management and Tuning
* Oracle Base: Alert Log and Trace Files
* Oracle Support:Understanding and Managing Server-Generated Alerts

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.

The execution plan shows a full table access for theSALEStable. To reduce the execution time, creating an index onSALES.TIME_IDwould be beneficial as it would allow the database to quickly sort and retrieve the most recent sales without the need to perform a full table scan, which is I/O intensive and slower. By indexing TIME_ID, which is used in theORDER BYclause, the optimizer can take advantage of the index to efficiently sort and limit the result set to the ten most recent sales.
* B (Incorrect):ReplacingFETCH FIRSTwithROWNUMwould not necessarily improve the performance unless there is an appropriate index that the optimizer can use to avoid sorting the entire result set.
* C (Incorrect):There is no indication that the current statistics are inaccurate; hence, collecting new statistics may not lead to performance improvement.
* D (Incorrect):While adaptive plans can provide performance benefits by allowing the optimizer to adapt the execution strategy, the main issue here is the lack of an index on theORDER BYcolumn.
* E (Incorrect):Creating an index onSALES.CUST_IDcould improve join performance but would not address the performance issue caused by the lack of an index on theORDER BYcolumn.
References:
* Oracle Database SQL Tuning Guide:Managing Indexes
* Oracle Database SQL Tuning Guide:Using Indexes and Clusters

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