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NEW QUESTION: 1
Which three statements are true about AutoInvoice? (Choose three.)
A. It determines whether the invoices should have open or closed status on the basis of the line amounts.
B. It determines the General Ledger (GL) accounts by usingAutoAccounting.
C. It imports invoices that have only transaction types that are of the "imported" transaction class.
D. It uses theAutoInvoice clearing account if the price multiplied by quantity does not equal the total amount for a line.
E. It determines the GL accounts from the rows in the RA_INTERFACE_DISTRIBUTIONS_ALL table.
F. It uses theAutoInvoice clearing account if it cannot determine the Receivables account by using AutoAccounting.
Answer: B,D,E

NEW QUESTION: 2
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

NEW QUESTION: 3
You have an Active Directory Rights Management Services (AD RMS) cluster.
You need to prevent users from encrypting new content. The solution must ensure that the users can continue to decrypt content that was encrypted already.
Which two actions should you perform? (Each correct answer presents part of the solution. Choose two.)
A. From the Active Directory Rights Management Services console, modify the rights policy templates.
B. From the Active Directory Rights Management Services console, create a user exclusion policy.
C. Modify the NTFS permissions of %systemdrive%\inetpub\wwwroot\_wmcs\decommission.
D. From the Active Directory Rights Management Services console, enable decommissioning.
E. Modify the NTFS permissions of %systemdrive%\inetpub\wwwroot\_wmcs\licensing.
Answer: C,D
Explanation:
Explanation/Reference:
* Decommissioning refers to the entire process of removing the AD RMS cluster and its associated databases from an organization. This process allows you to save rights-protected files as ordinary files before you remove AD RMS from your infrastructure so that you do not lose access to these files.
Decommissioning an AD RMS cluster is achieved by doing the following:
/Enable the decommissioning service. (A)
/Modify permissions on the decommissioning pipeline.
/Configure the AD RMS-enabled application to use the decommissioning pipeline.
* To modify the permissions on the decommissioning pipeline
1. Log on to ADRMS-SRV as cpandl\administrator.
2. Click Start, type %systemdrive%\inetpub\wwwroot\_wmcs in the Start Search box, and then press ENTER.
3. Right-click the decommission folder, and then click Properties.
4. Click the Security tab, click Edit, and then click Add. (D) Etc.
Reference: Step 1: Decommission AD RMS Root Cluster