Pass the Oracle Oracle Database 19c 1z0-084 Questions and answers with CertsForce

For this SQL statement and output, we can analyze the EVENT column to understand the type of wait:

B. The event "SQL*Net message from client" typically indicates that the session is waiting for a response from the client. This can be due to a network issue, user response, or an application processing delay.

E. The event "SQL*Net message from client" also implies that the session is idle waiting for the client (a user or an application) to send a request to the server. This event usually indicates that the session is not actively working but is instead waiting for the next command.

F. The wait event "enq: TX - row lock contention" suggests that session 9822 is waiting for a row-level lock held by another session. If the holding session issues a COMMIT or ROLLBACK, the lock will be released, and session 9822 will stop waiting. Since this session is experiencing row lock contention, it implies it's waiting for a specific transaction to complete.

References:

Oracle Database Reference, 19c

Oracle Wait Events Documentation

To transport performance data from an Oracle Database Standard Edition, which uses Statspack, to an Enterprise Edition database, which uses AWR, you must consider the compatibility of data structures and repository schemas between these tools. The recommended method is:

D (Correct): Export the data using the exp utility with a parameter file appropriate for Statspack (like spuexp.par) from the Statspack repository and import it into a dedicated Statspack schema on the destination. Since Statspack and AWR use different schemas, it's not recommended to import Statspack data directly into the AWR repository.

The other options are incorrect because:

A (Incorrect): expdp is not designed to export from Statspack, and awrload is intended for loading from an AWR export file, not a Statspack export.

B (Incorrect): Although expdp and impdp are used for exporting and importing data, the AWR repository schema is different from the Statspack schema, so importing Statspack data directly into the AWR repository is not recommended.

C (Incorrect): Using expdp to export from Statspack and then importing directly into the AWR repository is not the correct approach due to the schema differences between Statspack and AWR.

References:

Oracle Database Performance Tuning Guide: Migrating from Statspack to AWR

DB time includes the time spent on user and background processes. It can be greater than the elapsed time because it accumulates the active time of all the processes. For example, if two sessions are each active for 2 seconds at the same time, DB time would accumulate 4 seconds, while the elapsed time would be only 2 seconds.References:

Oracle Database Performance Tuning Guide, 19c

Oracle Database Reference, 19c

In this scenario, creating an index and generating frequency histograms are two methods that can potentially improve performance:

A (Correct): Generating frequency histograms on the CITY_ID column can help the optimizer make better decisions regarding the execution plan, especially if the data distribution is skewed. Histograms provide the optimizer with more detailed information about the data distribution in a column, which is particularly useful for columns with non-uniform distributions.

B (Correct): Creating an index on the CITY_ID column would speed up queries that filter on this column, especially if it's used frequently in the WHERE clause as a filter. An index would allow for an index range scan instead of a full table scan, reducing the I/O and time needed to execute such queries.

C (Incorrect): While SQL profiles can be used to improve the performance of specific SQL statements, they are usually not the first choice for such a problem, and creating a profile does not replace the need for proper indexing or statistics.

D (Incorrect): Forcing the subquery to use dynamic sampling might not provide a consistent performance benefit, especially if the table statistics are not representative or are outdated. However, dynamic sampling is not as effective as having accurate statistics and a well-chosen index.

E (Incorrect): Adaptive plans can adjust the execution strategy based on the conditions at runtime. While they can be useful in certain scenarios, in this case, creating an index and ensuring accurate statistics would likely provide a more significant performance improvement.

References:

Oracle Database SQL Tuning Guide: Managing Optimizer Statistics

Oracle Database SQL Tuning Guide: Using Indexes and Clusters

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.

References:

Oracle Database Reference, 19c

Oracle Database Performance Tuning Guide, 19c

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