Reasons to Choose Our Oracle 1Z0-084 Exam Dumps

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 the CURSOR_SHARING parameter to FORCE will 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): Setting OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES to TRUE can 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.

Oracle Database Performance Tuning Guide: Minimizing Hard Parses

Oracle Database SQL Tuning Guide: CURSOR_SHARING

The DBMS_WORKLOAD_REPOSITORY.CREATE_BASELINE_TEMPLATE procedure is used to create a repeating baseline template in the Automatic Workload Repository (AWR). This template will generate baselines for a specified duration of time on a repeating schedule. The parameters of the CREATE_BASELINE_TEMPLATE procedure include the start and end times, as well as the day of the week and hour in the day when the baseline should be captured.

Given that the command specifies a repeating baseline every Monday at 5 PM with a duration of 3 hours and it expires after 30 days, the number of baselines generated by this command that you can have at any given time depends on how many Mondays fall within the most recent 30-day period.

Since the maximum number of Mondays that can occur within any 30-day period is 5 (four to five weeks), but considering the baseline has a duration of 3 hours and starts every Monday at 5 PM, only one baseline for each Monday can exist at a time. However, since baselines are preserved for 30 days, you could have multiple instances of Monday baselines preserved at a time.

A (Incorrect): There can be more than one baseline at a time because the template will generate a baseline for every Monday during the 30-day expiration period.

B (Incorrect): There will be more than three baselines because the template creates a baseline for every Monday within the 30-day expiration period.

C (Correct): Over a 30-day period, considering the duration of the baselines and their frequency, you could have up to a maximum of 52 baselines if you consider the entire year.

D (Incorrect): There is no option that restricts the number of baselines to 5 specifically, the answer relies on the calculation of how many baselines can exist over a period of time considering their expiration.

Oracle Database PL/SQL Packages and Types Reference: DBMS_WORKLOAD_REPOSITORY

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.

Oracle Database SQL Tuning Guide: Managing Indexes

Oracle Database SQL Tuning Guide: Index Compression

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.

Oracle Database Utilities: Data Pump Export

Oracle Database Utilities: Data Pump Import

The cursor: pin S wait on X wait event suggests contention for a cursor pin, which is associated with mutexes (a type of locking mechanism) that protect the library cache to prevent concurrent modifications. This issue can often be alleviated by deferring the invalidation of cursors until the end of the call to reduce contention. The correct command to use would be:

C (Correct): ALTER SYSTEM SET CURSOR_INVALIDATION=DEFERRED; This setting defers the invalidation of dependent cursors until the end of the PL/SQL call, which can reduce the cursor: pin S wait on X wait events.

The other options are incorrect in addressing this issue:

A (Incorrect): Setting CURSOR_SHARING to FORCE makes the optimizer replace literal values with bind variables. It doesn't address the contention for cursor pins directly.

B (Incorrect): CURSOR_SPACE_FOR_TIME=TRUE aims to reduce the parsing effort by keeping cursors for prepared statements open. It may increase memory usage but does not directly resolve cursor: pin S wait on X waits.

D (Incorrect): Increasing SESSION_CACHED_CURSORS caches more session cursors but doesn't necessarily prevent the contention indicated by the cursor: pin S wait on X wait events.

Oracle Database Reference: CURSOR_INVALIDATION

Oracle Database Performance Tuning Guide: Reducing Cursor Invalidation