2026年現在、C-PAII10-35試験の出題内容は公式の改訂に伴って変わることがあり、古い教材での対策はリスクになりかねません。PassTestのSAP Certified Application Associate - SAP Predictive Analytics問題集は365日間の無料更新付きで、常に最新の内容で学習を続けられます。

SAP C-PAII10-35 試験概要:

SAP C-PAII10-35 試験シラバストピック:

C-PAII10-35 試験に関してよくいただくご質問

SAP Certified Application Associate - SAP Predictive Analytics 認定 C-PAII10-35 試験問題：

問題 #1

The application allows you to refine a currently open model. For
instance, you can:
Note: There are 2 correct answers to this question.

A. Generate a model of degree 2 using the most significant variables of the degree 1 model.
B. The server port (by default: 12347 for the authenticated server)
C. Reduce the number of explanatory variables used by the model while
maintaining the initial quality

問題 #2

A polynomial may be of degree 1, 2, 3 or greater. By
defining the polynomial degree, you are defining the degree of
complexity of the model. Examples of Polynomials:
Note: There are 2 correct answers to this question.

A. Xn A polynomial of degree 2 is of the form: f
B. The server private key
C. A polynomial of degree 1 is of the form: f
D. The server certificate

問題 #3

To describe your data, you can:
Note: There are 2 correct answers to this question.

A. Or create a description file using the Analyze option. In this case, it is important that you validate the description file obtained. You can save this file for later re-use.
B. Use an existing description file, that is, taken from your information system or saved from a previous use of Automated Analytics.
C. A web server such as Apache Web Server or Windows Internet
Information Services (IIS).

問題 #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 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.
B. 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,
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.

問題 #5

To Select a Profit Type ;
Note: There are 2 correct answers to this question.

A. The server port (by default: 12347 for the authenticated server).
B. Select a profit type. The corresponding profit curves appear.
C. On the screen Model Curves, beneath the plot, click the dropdown list associated with the Profit field.
The list of profit types appears.

解説：