1Z0-084 Exam Dumps and Practice Questions

The right time to stop tuning an Oracle database is often determined by the point of diminishing returns - when the cost of further tuning (in terms of time, resources, or money) exceeds the performance benefits gained. This is often related to the budget allocated for performance tuning.

A (Correct): When the allocated budget for performance tuning has been exhausted, it may be time to stop tuning unless the benefits of further tuning justify requesting additional budget.

B (Incorrect): Eliminating all concurrency waits from the Top 10 is an unrealistic goal since some waits are inevitable and can occur due to application design, which might not be possible to eliminate completely.

C (Incorrect): The buffer cache and library cache hit ratio being above 95% does not necessarily indicate that the database is fully optimized. Hit ratios are not reliable indicators of database performance and should not be used as sole criteria to end tuning efforts.

D (Incorrect): Having I/O less than 10% of DB time is not a definitive indicator to stop tuning. It is essential to consider the overall performance goals and whether they have been met rather than focusing solely on I/O metrics.

Oracle Database Performance Tuning Guide: Introduction to Performance Tuning

Oracle Database 2 Day + Performance Tuning Guide: Understanding the Tuning Process

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

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.

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

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.

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.

Given a server with 64 CPUs, if the buffer cache size increase did not alleviate free buffer waits and buffer busy waits, one can look into optimizing I/O and the efficiency of the DB writer processes.

C) Setting the DBWR_IO_SLAVES parameter to a non-zero value, such as the number of CPUs, would initiate I/O slave processes to assist the DB writer process. This can help reduce I/O contention when writing from the buffer cache to disk, particularly for systems without asynchronous I/O capabilities.

D) Increasing the value of DBWRITERPROCESSES enables multiple DB writer processes to be active simultaneously. In a system with many CPUs, such as 64, increasing this value can improve the write throughput to disk and potentially reduce buffer busy waits.

Oracle Database Reference, 19c

Oracle Database Performance Tuning Guide, 19c