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Oracle Database 19c Performance and Tuning Management Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which three statements are true about using the in Memory (IM) column store?

A. It improves performance for queries joining several tables using bloom filter joins.
B. It does not improve performance for queries that use join groups on columns from different tables.
C. It does not improve performance for queries using cached results of function evaluations on columns from the same table.
D. It can improve OLTP workload performance by avoiding the use of indexes.
E. It does not improve performance for queries using user-defined virtual column results.
F. It does not require all database data to fit in memory to improve query performance.

Answer: A,D,F

Explanation:
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

NEW QUESTION # 43
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?

A. Partition the SALES table.
B. Coalesce all sales table indexes.
C. Re-create the SALES table.
D. Compress the SALES table.
E. Increase PCTFREE for the SALES table.

Answer: A,D

Explanation:
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.
References:
* 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.

NEW QUESTION # 44
Examine this output of a query of VSPGA_TAPGET_ADVICE:

Which statements is true'

A. GGREGATE_TARGET should be set to at least 700 MB.
B. With a target of 700 MB or more, all multipass executions work areas would be eliminated.
C. With a target of 800 MB or more, all one-pass execution work areas would be eliminated.
D. PGAA_AGGREGATE should be set to at least 800 MB.

Answer: B

Explanation:
The V$PGA_TARGET_ADVICE view provides advice on potential performance improvements by adjusting the PGA_AGGREGATE_TARGET parameter. The column ESTD_OVERALLOC_COUNT indicates the estimated number of work areas that would perform multiple passes if the PGA_AGGREGATE_TARGET were set to the size in the TARGET_MB column.
A: According to the output, at the target of 700 MB, the ESTD_OVERALLOC_COUNT is 30. This suggests that if PGA_AGGREGATE_TARGET is set to 700 MB, 30 multipass execution work areas would be required. If we look further down, at the target of 800 MB, the ESTD_OVERALLOC_COUNT is 0, indicating that increasing PGA_AGGREGATE_TARGET to 800 MB or more would eliminate the need for multipass executions, not at 700 MB as initially suggested by the option. Hence, the verified answer derived from the data is slightly nuanced; it should be 800 MB to eliminate all multipass executions.
References:
* Oracle Database Performance Tuning Guide, 19c
* Oracle Database Reference, 19c

NEW QUESTION # 45
Examine this command:

What is the maximum number of baselines generated by this command that you can have at any given time?

A. 0
B. 1
C. 2
D. 3

Answer: D

Explanation:
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.
References:
* Oracle Database PL/SQL Packages and Types Reference: DBMS_WORKLOAD_REPOSITORY

NEW QUESTION # 46
Which three statements are true about tuning dimensions and details of v$sys_time_model and DB time?

A. Parse Time Elapsed accounts for successful soft and hard parse operations only.
B. The proportion of WAIT TIME to CPU TIME always increases with increased system load.
C. DB Time accounts for all time used by background processes and user sessions.
D. Systems in which CPU time is dominant need more tuning that those in which WAIT TIME is dominant.
E. When WAIT TIME is high, instance tuning may improve performance.
F. Statspack cannot account for high CPU time when CPU TIME is a Top 10 event in DB time. When CPU time is high, SQL tuning may improve performance.

Answer: C,E,F

Explanation:
A: Statspack is a performance diagnostic tool that can help identify high CPU usage issues. High CPU time may indicate that SQL statements need to be tuned for better performance.
D: High wait times can often be reduced by instance tuning, such as adjusting database parameters or improving I/O performance.
F: DB Time is a cumulative time metric that includes the time spent by both user sessions and background processes executing database calls.References:
* Oracle Database Performance Tuning Guide, 19c
* Oracle Database Concepts, 19c

NEW QUESTION # 47
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