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NEW QUESTION: 1
A. Option A
B. Option D
C. Option C
D. Option B
Answer: D
NEW QUESTION: 2
A. CLSD
B. TECO
C. CNF
D. PCNF
E. CRTD
Answer: A,B,D
NEW QUESTION: 3
D：Deploy.jsonという名前のテンプレートをDeploy-lod9940427という名前のリソースグループに展開する予定です。
次の要件を満たすようにテンプレートを変更してから、テンプレートを展開する必要があります。
*合計256個のIPアドレスのみをサポートするには、アドレススペースを削減する必要があります。
*合計64個のIPアドレスのみをサポートするには、サブネットアドレススペースを縮小する必要があります。
このタスクを完了するには、Microsoft Azureポータルにサインインします。
Answer:
Explanation:
See solution below.
Explanation
1. Sign in to the portal,
2. Choose template Deploy-lod9940427
3. Select Edit template, and then paste your JSON template code into the code window.
4. Change the ASddressPrefixes to 10.0.0.0/24 in order to support only 256 total IP addresses.
addressSpace":{"addressPrefixes": ["10.0.0.0/24"]},
5. Change the firstSubnet addressprefix to 10.0.0.0/26 to support only 64 total IP addresses.
"subnets":[
{
"name":"firstSubnet",
"properties":{
"addressPrefix":"10.0.0.0/24"
}
6. Select Save.
7. Select Edit parameters, provide values for the parameters that are shown, and then select OK.
8 Select Subscription. Choose the subscription you want to use, and then select OK.
9. Select Resource group. Choose an existing resource group or create a new one, and then select OK.
10. Select Create. A new tile on the dashboard tracks the progress of your template deployment.
References:
https://docs.microsoft.com/en-us/azure-stack/user/azure-stack-deploy-template-portal?view=azs-1908
https://docs.microsoft.com/en-us/azure/architecture/building-blocks/extending-templates/update-resource
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. Oriented
B. Deviation
C. Classification
D. Probabilistic
Answer: A
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
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