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NEW QUESTION: 1
予測精度を測定し、これらの測定値がアイテム組織レベルからより高いレベルにどのように集約されるかを指定するために使用できるKPI。
A. MAPE、MAD、およびBiasは、予測精度の測定に使用されます。 MADとバイアスは合計を使用して集計されます。 MAPEは、加重平均を使用して集計されます。
B. MAPE、MFE、およびバイアスは、予測精度の測定に使用されます。 MAPEとMFEは、加重平均を使用して集計されます。バイアスは合計を使用して集計されます。
C. MAPE、MAD、およびBiasは、予測精度の測定に使用されます。 MAPEとバイアスは、加重平均を使用して集計されます。 MADは合計を使用して集計されます。
D. MAPE、MFE、およびバイアスは、予測精度の測定に使用されます。 MAPEとバイアスは、加重平均を使用して集計されます。 MFEは合計を使用して集計されます。
Answer: A
NEW QUESTION: 2
What is the unique partitioning feature of nPars?
A. I/O sharing in cell-based servers
B. Storage sharing in blade-based servers
C. Electrical isolation from other partitions
D. VM isolation in blade-based servers
Answer: C
Explanation:
Explanation/Reference:
Reference:
http://en.wikipedia.org/wiki/HP_Hard_Partitioning
NEW QUESTION: 3
Examine the parameters for your database instance:
Which three statements are true about the process of automatic optimization by using cardinality feedback?
(Choose three.)
A. The optimizer enables monitoring for cardinality feedback after the first execution of a query.
B. The optimizer automatically changes a plan during subsequent execution of a SQL statement if there is a huge difference in optimizer estimates and execution statistics.
C. The optimizer does not monitor cardinality feedback if dynamic sampling and multicolumn statistics are enabled.
D. The optimizer can re optimize a query only once using cardinality feedback.
E. After the optimizer identifies a query as a re-optimization candidate, statistics collected by the collectors are submitted to the optimizer.
Answer: A,B,C
Explanation:
Explanation
C: During the first execution of a SQL statement, an execution plan is generated as usual.
D: if multi-column statistics are not present for the relevant combination of columns, the optimizer can fall back on cardinality feedback.
(not B)* Cardinality feedback. This feature, enabled by default in 11.2, is intended to improve plans for repeated executions.
optimizer_dynamic_sampling
optimizer_features_enable
* dynamic sampling or multi-column statistics allow the optimizer to more accurately estimate selectivity of conjunctive predicates.
Note:
* OPTIMIZER_DYNAMIC_SAMPLING controls the level of dynamic sampling performed by the optimizer.
Range of values. 0 to 10
* Cardinality feedback was introduced in Oracle Database 11gR2. The purpose of this feature is to automatically improve plans for queries that are executed repeatedly, for which the optimizer does not estimate cardinalities in the plan properly. The optimizer may misestimate cardinalities for a variety of reasons, such as missing or inaccurate statistics, or complex predicates. Whatever the reason for the misestimate, cardinality feedback may be able to help.
NEW QUESTION: 4
A table in one ofi your database schemas contains only varchar, number, and date data types fior the columns.
Which three operations can be offloaded to the Exadata storage servers when doing a smart scan against this table, ifi
no other situations arise that prevent Smart Scan firom occurring?
A. Hash join filtering
B. Virtual column filtering
C. Predicate filtering
D. Column filtering
E. Sort merge join filtering
F. Nested loop Join filtering
Answer: A,C,D
Explanation:
A: Smart Scan Column Filtering
Exadata provides column filtering, also called column projection, fior table scans. Only the columns requested are
returned to the database server rather than all columns in a table. For example, when the fiollowing SQL is issued, only
the employee_name and employee_number columns are returned firom Exadata to the database kernel.
SELECT employee_name, employee_number FROM employee_table.
For tables with many columns, or columns containing LOBs (Large Objects), the I/O bandwidth saved can be very
large. Using both predicate and column filtering dramatically improves perfiormance and reduces I/O bandwidth
consumption. In addition, column filtering also applies to indexes, allowing fior even fiaster query perfiormance.
C: Smart Scan Predicate Filtering
Exadata enables predicate filtering fior table scans. Only the rows requested are returned to the database server
rather than all rows in a table. For example, when the fiollowing SQL is issued only rows where the employees' hire
date is after the specified date are sent firom Exadata to the database instance.
SELECT * FROM employee_table WHERE hire_date > '1-Jan-2003'.
This ability to return only relevant rows to the server greatly improves database perfiormance. This perfiormance
enhancement also applies as queries become more complicated, so the same benefits also apply to complex queries,
including those with subqueries.