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During which application lifecycle phase do you take baselines?
Baselines are typically taken during the production phase of the application lifecycle. They provide a snapshot of performance metrics under normal operating conditions which can be used for comparison against future performance. Baselines are essential for understanding how the system performs under its typical workload and for detecting deviations from this expected performance over time, especially after changes like migrations, upgrades, or significant changes in user activity.
Reference
Oracle Database 19c Performance Tuning Guide - Managing Performance Through Baselines
You manage a 19c database with default optimizer settings.
This statement is used extensively as subquery in the application queries:
SELECT city_id FROM sh2.sales WHERE city_id=:Bl
You notice the performance of these queries is often poor and, therefore, execute:
SELECT city_id,COUNT(*) FROM sh2.sales GROUP BY city_id;
Examine the results:
There is no index on the CITY_ID column.
Which two options improve the performance?
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might not provide a consistent performance benefit, especially if the table statistics are not representative or are outdated.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.
Oracle Database SQL Tuning Guide: Managing Optimizer Statistics
Oracle Database SQL Tuning Guide: Using Indexes and Clusters
A Standard Edition production database has performance problems for two hours on the same day each week.
Which tool must you use to diagnose the problem?
For a Standard Edition production database, the Statspack tool is available to diagnose performance problems. The Automatic Workload Repository (AWR) and its related tools like AWR Compare Periods report and SQL Performance Analyzer are features of the Oracle Database Enterprise Edition and are not available in Standard Edition. Database Replay is also a feature of the Enterprise Edition. Statspack is a performance diagnostic tool provided for earlier versions and Standard Editions of the Oracle Database to collect, store, and analyze performance data.
Reference
Oracle Database 19c Administrator's Guide - Using Statspack to Diagnose Database Performance Issues
Accessing the SALES tables causes excessive db file sequential read wait events.
Examine this AWR except:
Now, examine these attributes displayed by querying dba_tables:
Finally, examine these parameter settings:
Which two must both be used to reduce these excessive waits?
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.
Examine this statement and its corresponding execution plan:
Which phase introduces the CONCATENATION step?
The CONCATENATION step in an execution plan is introduced during the SQL Transformation phase. This phase is part of the optimizer's query transformations which can include various techniques to rewrite the query for more efficient execution. The CONCATENATION operation is used to combine the results of two separate SQL operations, typically when there is an OR condition in the WHERE clause, as seen in the provided query.
Oracle Database SQL Tuning Guide, 19c
Oracle Database Concepts, 19c
