Free 1Z0-084 sample questions

Correct answer: Identify and tune the SQL statements to use a more selective index or a full table scan.

'latch: cache buffers chains' contention often occurs when multiple sessions are trying to access blocks protected by the same latch, a situation commonly caused by 'hot blocks'. This is frequently a result of inefficient SQL, such as repeatedly scanning a large number of blocks through a non-selective index. The most effective solution is to fix the root cause: the SQL statement. Tuning the SQL to use a more selective index or a full table scan (if appropriate) will reduce the logical I/O and the contention on the same set of blocks. Increasing the buffer cache might slightly alleviate the symptoms but doesn't solve the underlying SQL inefficiency. Reducing block size is a major architectural change and not a direct solution. Increasing _DB_BLOCK_LRU_LATCHES is a deprecated action and not recommended.

Correct answer: The /dev/shm filesystem on the Linux server is sized smaller than the MEMORY_TARGET value.

The ORA-00845 error (MEMORY_TARGET not supported on this system) indicates that the operating system environment cannot support the Automatic Memory Management configuration. On Linux systems, AMM requires the /dev/shm shared memory filesystem to be large enough to hold the entire MEMORY_TARGET allocation. If /dev/shm is smaller than MEMORY_TARGET, the database instance cannot allocate the required shared memory and will fail to start. The correct resolution is to increase the size of /dev/shm (typically by editing /etc/fstab) to be at least the size of MEMORY_TARGET and reboot the server or remount the filesystem.

Correct answers: Database Replay; SQL Performance Analyzer (SPA)

Correct answer: A function or an implicit data type conversion is being applied to the partitioning key column in the WHERE clause.

Partition pruning can only occur if the optimizer can determine at parse time which partitions need to be accessed. If a function (e.g., TO_CHAR, SUBSTR) or an implicit data type conversion is applied to the partitioning key column in the WHERE clause, the optimizer cannot make this determination. For example, if a DATE column is the partitioning key and the WHERE clause compares it to a character string (`WHERE partition_date_col = '01-JAN-2023'`), the implicit TO_DATE conversion on the literal value is fine. However, if the query is written as `WHERE TO_CHAR(partition_date_col, 'YYYY-MM-DD') = '2023-01-01'`, the function on the column prevents pruning. This is a common cause of performance issues with partitioned tables.

Correct answer: True

This statement is true. Oracle Database allows you to set a default In-Memory attribute for a tablespace. When you create a new table or partition within that tablespace without specifying an `INMEMORY` or `NO INMEMORY` clause, it inherits the default setting from the tablespace. This simplifies the management of In-Memory objects for large applications.

Correct answer: Examine the `redo write time` and `avg wrt(ms)` statistics in an AWR report.

The 'log file sync' wait event indicates that user sessions are waiting for LGWR to write redo from the log buffer to the online redo logs. The 'log file parallel write' event is the time LGWR itself spends writing to the logs. To determine if the I/O subsystem is the bottleneck, you must measure the actual write performance. The AWR report's I/O stats section, specifically the 'redo write time' and average write time (`avg wrt(ms)` for the redo log files), provides a direct measurement of the I/O performance for redo writes. High values here (e.g., >10ms) confirm an I/O bottleneck. Increasing the log buffer is unlikely to help if the I/O subsystem cannot keep up. Increasing log file size might reduce log switches but won't improve write speed. Adding more redo log groups is a good practice for availability but also doesn't directly speed up individual writes.

Correct answer: To store a set of auxiliary statistics and corrections that guide the optimizer to generate a better execution plan.

A SQL Profile does not freeze an execution plan like a SQL Plan Baseline does. Instead, it contains supplemental statistics and correction factors (e.g., adjustments to cardinality or cost estimates) derived from the SQL Tuning Advisor's analysis. When the SQL statement is parsed, the optimizer uses this additional information from the profile, along with the regular object statistics, to make better decisions and generate a more optimal plan. This allows the plan to adapt to future changes in data or statistics while still being guided by the profile's corrections.

Correct answer: Query the `V$PGA_TARGET_ADVICE` view.

The `V$PGA_TARGET_ADVICE` view is specifically designed to help DBAs size the `PGA_AGGREGATE_TARGET`. It predicts how changes to this parameter will affect the cache hit percentage and the number of multipass executions. By examining the `ESTD_OVERALLOC_COUNT` or `ESTD_MULTIPASS_EXECUTIONS` (in older versions) columns, the DBA can see at which `PGA_AGGREGATE_TARGET` value the number of multipass executions would drop to zero or an acceptable level. This provides a direct, data-driven recommendation for tuning the PGA.

Correct answers: Implement a reverse key index on the primary key.; Partition the table using hash partitioning on the primary key.

Correct answer: Implement hash partitioning on the `INVENTORY` table using the `PRODUCT_ID` column as the partition key.

The high 'gc current block 2-way' waits on the `INVENTORY` table indicate severe block contention between the RAC nodes. This happens when both nodes are frequently requesting the most current version of the same data blocks for modification. The root cause is that updates for different products are likely physically co-located in the same data blocks, causing inter-node conflicts. Implementing hash partitioning on `PRODUCT_ID` will physically separate the data for different products into different partitions, and therefore different sets of data blocks. This dramatically reduces the probability that Node 1 and Node 2 will need to modify the same block at the same time, thus mitigating the global cache contention. Directing traffic to one node would serialize the workload, defeating the purpose of RAC for scalability. Increasing DBWRs doesn't solve the inter-node block transfer issue. Rebuilding indexes might temporarily help but won't solve the fundamental data placement problem.