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NEW QUESTION: 1
AWSによって完全に管理される共有責任モデルのコンポーネントは次のうちどれですか？
A. 多要素認証の実施
B. オペレーティングシステムソフトウェアのパッチ
C. データの暗号化
D. 物理的なデータセンター資産の監査
Answer: D
Explanation:
説明
もちろん、AmazonはAmazon Inc.の所有物であるため、物理的なデータセンターの資産とリソースの監査を担当します。お客様は物理的なサイトにアクセスできないため、物理的なデータセンターの資産を維持する責任はありません。

NEW QUESTION: 2
You need to display the first names of all customers from the customers table that contain the character 'e' and have the character 'a' in the second last position.
Which query would give the required output?

A. Option B
B. Option C
C. Option D
D. Option A
Answer: D
Explanation:
The SUBSTR(string, start position, number of characters) function accepts three parameters and returns a string consisting of the number of characters extracted from the source string, beginning at the specified start position:
substr('http://www.domain.com', 12, 6) = domain
The position at which the first character of the returned string begins.
When position is 0 (zero), then it is treated as 1.
When position is positive, then the function counts from the beginning of string to find the first
character.
When position is negative, then the function counts backward from the end of string.
substring_length
The length of the returned string. SUBSTR calculates lengths using characters as defined by the
input character set. SUBSTRB uses bytes instead of characters. SUBSTRC uses Unicode
complete characters.
SUBSTR2 uses UCS2 code points. SUBSTR4 uses UCS4 code points.
When you do not specify a value for this argument, then the function
The INSTR(source string, search item, [start position], [nth occurrence of search item]) function returns a number that represents the position in the source string, beginning from the given start position, where the nth occurrence of the search item begins:
instr('http://www.domain.com', '.', 1, 2) = 18

NEW QUESTION: 3
Using the default the 5875 SRDF/A cycle time, what is the recovery point objective in seconds?
A. 0
B. 1
C. 2
D. 3
Answer: D

NEW QUESTION: 4
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. Classification
B. Deviation
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