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NEW QUESTION: 1








Answer:
Explanation:

Explanation:

Step 1:
Restore page
Start a page restore with a full database, file, or filegroup backup that contains the page. In the RESTORE DATABASE statement, use the PAGE clause to list the page IDs of all of the pages to be restored.
Step 2:
Restore log file with norecovery. Use the first file (FILE = 1).
Step 3:
Backup the tail-end of the log.
Create a new log backup of the database that includes the final LSN of the restored pages, that is, the point at which the last restored page is taken offline.
Step 4:
Restore database with recovery. Use second file (FILE = 2).
Restore the new log backup. After this new log backup is applied, the page restore is completed and the pages are now usable.
Example:
The following example restores four damaged pages of file B with NORECOVERY. Next, two log backups are applied with NORECOVERY, followed with the tail-log backup, which is restored with RECOVERY. This example performs an online restore. In the example, the file ID of file B is 1, and the page IDs of the damaged pages are 57, 202, 916, and 1016.
RESTORE DATABASE <database> PAGE='1:57, 1:202, 1:916, 1:1016'
FROM <file_backup_of_file_B>
WITH NORECOVERY;
RESTORE LOG <database> FROM <log_backup>
WITH NORECOVERY;
RESTORE LOG <database> FROM <log_backup>
WITH NORECOVERY;
BACKUP LOG <database> TO <new_log_backup>;
RESTORE LOG <database> FROM <new_log_backup> WITH RECOVERY;
References: https://docs.microsoft.com/en-us/sql/relational-databases/backup- restore/restore-pages-sql-server

NEW QUESTION: 2
Many approaches to Knowledge Discovery in Databases (KDD) are used to identify valid and useful patterns in data. This is an evolving field of study that includes a variety of automated analysis solutions such as Data Mining. Which of the following is not an approach used by KDD?
A. Deviation
B. Classification
C. Probabilistic
D. Oriented
Answer: D
Explanation:
The Oriented approach does not correctly describe a KDD approach.
The main approaches of KDD according to CBK are:
* Probabilistic approach: uses graphical representation models to compare different knowledge representations. The models are based on probabilities and data independencies. The probabilistic models are useful for applications involving uncertainty, such as those used in planning and control systems.
* Statistical approach: uses rule discovery and is based on data relationships. Learning algorithm can automatically select useful data relationship paths and attributes. These paths and attributes are then used to construct rules for discovering meaningful information. This approach is used to generalize patterns in the data and to construct rules from the noted patterns. An example of the statistical approach is OLAP.
* Classification approach: groups data according to similarities. One example is a pattern discovery and data-cleaning model that reduces a large database to only a few specific records. By eliminating redundant and non-important data, the discovery of patterns in the data is simplified.
* Deviation and trend analysis: uses filtering techniques to detect patterns. An example is an intrusion detection system that filters a large volume of data so that only the pertinent data is analyzed.
* Neural networks: methods used to develop classification, regression, association, and segmentation models. A neural net method organizes data into nodes that are arranged in layers, and links between the nodes have specific weighting classifications. The neural net is helpful in detecting the associations among the input patterns or relationships. It is also considered a learning system because new information is utomatically incorporated into the system. However, the value and relevance of the decisions made by the neural network are only as good as the experience it is given. The greater the experience, the better the decision. Note that neural nets have a specific problem in terms of an individual's ability to substantiate processing in that they are subject to superstitious knowledge, which is a tendency to identify relations when no relations actually exist. More sophisticated neural nets are less subject to this problem.
* Expert system approach: uses a knowledge base (a collection of all the data, or knowledge, on a particular matter) and a set of algorithms and/or rules that infer new facts from knowledge and incoming data. The knowledge base could be the human experience that is available in an organization. Because the system reacts to a set of rules, if the rules are faulty, the response will also be faulty. Also, because human decision is removed from the point of action, if an error were to occur, the reaction time from a human would be longer.
* Hybrid approach: a combination of more than one approach that provides a more powerful and useful system.
The following answers are incorrect:
The other options describes some of the KDD possible approaches but were not the right choice.
The following reference(s) were/was used to create this question:
OFFICIAL (ISC)2 GUIDE TO THE CISSP EXAM - First Edition, page 309.
and
https://en.wikipedia.org/wiki/Data_mining

NEW QUESTION: 3
これにより、ログの記録から、Webセルフサービスやライブエージェントサポートなど、複数のチャネルにわたるソリューションの提案と提供まで、カスタマーサービスのライフサイクル全体を管理できます。
A. Ideas
B. Community
C. Service Cloud2
D. None of the Above
E. Sales Cloud
F. Sales Cloud2
Answer: C

NEW QUESTION: 4
When working directly with SOAP, which two statements are true about the encoding of compound values?
(Choose two.)
A. A compound value may NOT contain other compound values.
B. Accessors with names that are local to their containing types have unqualified names.
C. Compound values cannot be encoded.
D. Members of a compound value are encoded as accessor elements.
Answer: B,D

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