SAP 공인 인증인 SAP Certified Application Associate - SAP Predictive Analytics은 IT 업계에서 널리 인정받는 자격입니다. ExamPassdump의 C_PAII10_35 연습문제 80문항으로 그 가치에 걸맞은 실력을 갖추시기 바랍니다.

SAP C_PAII10_35 시험 개요:

SAP C_PAII10_35 시험 요강 주제:

SAP C_PAII10_35 시험에 대한 질문과 답변 모음

최신 SAP Certified Application Associate C_PAII10_35 무료샘플문제

문제 #1

How can you refine your model in the Select
Variables tool in Automated Analytics?
Note: There are 2 correct answers to this question.

A. Display variable correlations.
B. Simulate the model application.
C. Select the model iteration.
D. Analyze variable deviations.

문제 #2

The term model carries many different meanings depending on its field of application. In data mining, a model describes and explains the relationships that exist between input data (one or more target variablest allows one to predict and explain phenomena, or to describe them. To quote George E.P. Box,
"All Models Are Wrong But Some Are Useful" from "Robustness is the Strategy of Scientific Model Building" in Robustness in Statistics. Eds., R.L. Launer and G.N. Wilkinson, 1979, Academic Press. A model that has satisfactory performance is one that possesses both:
Note: There are 2 correct answers to this question.

A. Both of these ports must be accessible from the client machine. For example, network firewalls should allow communication on these ports between client machine and the servers.
B. High robustness, that is, sufficient capacity to repeat the same performance on new datasets containing observations of a similar nature to the training dataset. This explanatory power is indicated by the prediction confidence .
C. High explanatory power, that is, sufficient capacity to explain the target variable. This explanatory power is indicated by the predictive power of the model.

문제 #3

Once the model has been generated, you must verify its validity by examining the performance indicators:
Note: There are 4 correct answers to this question.

A. A robustness indicator KR equal to 0.992.
B. The existing R is uninstalled and the registry entries and the R installation folder are removed from the machine.
C. The prediction confidence defines the degree of robustness of the model, that is, its capacity to achieve the same explanatory power when applied to a new dataset. In other words, the degree of robustness corresponds to the predictive power of the model applied to an application dataset. For this scenario, the model generated has the following performance indicators:
D. The predictive power allows you to evaluate the explanatory power of the model, that is, its capacity to explain the target variable when applied to the training dataset. A perfect model would possess a predictive power equal to 1 and a completely random model would possess a predictive power equal to 0.
E. A quality indicator KI equal to 0.808,

문제 #4

Once the model has been generated, you must verify its validity by examining the performance indicators:
Note: There are 2 correct answers to this question.

A. The environment variable definition is using the POSIX standard shell sh semantic, which first defines the variable, then exports it. To guarantee a proper functioning, The environment variable definition is using the POSIX standard shell sh semantic, which first defines the variable, then exports it. To guarantee a proper functioning,
B. The predictive power allows you to evaluate the explanatory power of the model, that is, its capacity to explain the target variable when applied to the training dataset. A perfect model possesses a predictive power of 1 and a completely random model possesses a predictive power of 0. No minimum threshold is required for the predictive power of a model. This depends upon the context of your work, that is, your domain of application, the nature of your data and your business issue. 26 P U B L I C Automated Analytics User Guides and Scenarios Modeling Concepts In some cases, a model with a predictive power as low as 0.1 may allow realization of a profit of several thousands dollars. In all cases, a positive predictive power indicates that the model generated will perform better than a random model.
C. The prediction confidence defines the degree of robustness of the model, that is, its capacity to achieve the same explanatory power when applied to a new dataset. In other words, the degree of robustness corresponds to the predictive power of the model when applied to an application dataset. A model with a prediction confidence inferior to 0.95 must be considered with caution. The performance of such a model is very likely to vary between the training dataset and the application datasets.

문제 #5

The different types of variables: continuous, ordinal and nominal are next encoded by the data encoding feature of Modeler, or by the Event Logging and Sequence Coding features in the case of dynamic data.
Before generating the model, you must:
Note: There are 2 correct answers to this question.

A. Describe the data. A utility integrated with the application allows you to generate a description of the dataset to be analyzed, automatically. You need only validate that description, verifying that the type and storage format of each variable were identified correctly.
B. Users are authenticated because clients must log in before being able to use the modeling server. User accounts can be configured to implement security policy.
C. Resources are used more fully because each modeling session has a dedicated process. The process size limit applies only to a single user
D. Define the role of variables contained in the dataset to be analyzed. You may select one or more variables as target variables. These are the variables that corresponds with your business issue. The other variables of the table of data are considered to be explanatory variables: they allow calculation of the value of the target variable in a given context. They may also be used as weight variables.

질문과 대답: