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NEW QUESTION: 1
Drag and drop the OSPF adjacency states from the left onto the correct definitions on the right.

Answer:
Explanation:

NEW QUESTION: 2
An application has been through a peer review and regression testing and is prepared for release. A security engineer is asked to analyze an application binary to look for potential vulnerabilities prior to wide release. After thoroughly analyzing the application, the engineer informs the developer it should include additional input sanitation in the application to prevent overflows. Which of the following tools did the security engineer MOST likely use to determine this recommendation?
A. Vulnerability scanner
B. HTTP interceptor
C. SCAP scanner
D. Fuzzer
Answer: D

NEW QUESTION: 3
Which two types of column filtering may benefit from partition pruning?
A. Greater than operators on hash-partitioned tables
B. Equally operates on range-partitioned tables.
C. Equality operators on system-partitioned tables
D. Operators on range-partitioned tables
E. In-list operators on system-partitioned tables
Answer: B,D
Explanation:
The query optimizer can perform pruning whenever a WHERE condition can be reduced to either one of the following two cases:
partition_column = constant
partition_column IN (constant1, constant2, ..., constantN)
In the first case, the optimizer simply evaluates the partitioning expression for the value given, determines which partition contains that value, and scans only this partition. In many cases, the equal sign can be replaced with another arithmetic comparison, including <, >, <=, >=, and <>. Some queries using BETWEEN in the WHERE clause can also take advantage of partition pruning.
Note:
*The core concept behind partition pruning is relatively simple, and can be described as "Do not scan partitions where there can be no matching values".
When the optimizer can make use of partition pruning in performing a query, execution of the query can be an order of magnitude faster than the same query against a nonpartitioned table containing the same column definitions and data.
*Example:
Suppose that you have a partitioned table t1 defined by this statement:
CREATE TABLE t1 (
fname VARCHAR(50) NOT NULL,
lname VARCHAR(50) NOT NULL,
region_code TINYINT UNSIGNED NOT NULL,
dob DATE NOT NULL
)
PARTITION BY RANGE( region_code ) (
PARTITION p0 VALUES LESS THAN (64),
PARTITION p1 VALUES LESS THAN (128), PARTITION p2 VALUES LESS THAN (192), PARTITION p3 VALUES LESS THAN MAXVALUE ); Consider the case where you wish to obtain results from a query such as this one: SELECT fname, lname, region_code, dob FROM t1 WHERE region_code > 125 AND region_code < 130; It is easy to see that none of the rows which ought to be returned will be in either of the partitions p0 or p3; that is, we need to search only in partitions p1 and p2 to find matching rows. By doing so, it is possible to expend much less time and effort in finding matching rows than would be required to scan all partitions in the table. This"cutting away" of unneeded partitions is known as pruning.