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NEW QUESTION: 1
An end user adds a Secure Web Authentication (SWA) application to Okta. The end user signs in to the SWA application for the first time from the Okta end user dashboard. Is this an expected behavior?
Solution: Okta prompts the end user to confirm that the login attempt was successful.
A. No
B. Yes
Answer: A

NEW QUESTION: 2
You are developing a BI Semantic Model (BISM) that will be used to analyze complex budgeting and forecast data sourced from a financial database. The model will be deployed to a server with 28 GB of RAM.
The source data, located in a SQL Server data warehouse, is currently using 15 terabytes of disk space and is doubling in size every month. The model will be queried by staff in the accounting department by using Microsoft Excel 2010.
You need to ensure the highest query performance and scalability for the accounting department queries.
Which project type should you choose?
A. Tabular project that uses the In-Memory query mode
B. Multidimensional project
C. PowerPivot workbook deployed to SharePoint
D. Tabular project that uses the DirectQuery query mode
Answer: B

NEW QUESTION: 3
Which of the following is true in relation to Principal Component Analysis (PCA)?
I. An n x n positive definite square matrix will have n-1 eigenvectors
II. The eigenvalues for a correlation matrix can be derived from the corresponding values for the covariance matrix III. Principal components are uncorrelated to each other IV. PCA is useful as it allows 100% of the variation in a complex system to be explained by the first three principal components
A. III
B. I, II and IV
C. III and IV
D. I and III
Answer: A
Explanation:
Explanation
An n x n positive definite square matrix will have n eigenvectors, and not n - 1. Therefore statement I is incorrect.
A correlation and covariance matrix are related to each other through the matrix of standard deviations. If the covariance matrix is represented by V, the correlation matrix by C and D is the diagonal matrix of standard deviations, then V = DCD. However, there is no simple relationship between the eigenvalues of the two matrices, and it is not possible to derive the eigenvalues for one given the eigenvalues for the other. Therefore statement II is false.
Principal components are uncorrelated to each other. That is correct, and in fact PCA is useful because of this being so. Statement III is therefore true.
PCA does not explain 100% of the variation in a system with just three components - statement IV is false.
(Remember though that most (though not 100%) of the variation in a system of term structures is explained by the first three components - trend, tilt and curvature).
Thus Choice 'd' is the correct answer.