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HCSA-Presales-Smart PV(Residential) V1.0
- Exam Number/Code : H19-389_V1.0
- Exam Name : HCSA-Presales-Smart PV(Residential) V1.0
- Questions and Answers : 213 Q&As
- Update Time: 2019-01-10
- Price:
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NEW QUESTION: 1
An administrator intends to configure an IPSec solution that provides ESP with integrity protection, but not confidentiality protection. Which of the following AES modes of operation would meet this integrity-only requirement?
A. CFB
B. GMAC
C. GCM
D. PCBC
E. CBC
Answer: B
NEW QUESTION: 2
Which UEFI shell command should be used to verify the firmware on the HP Integrity Blade LOMs and installed mezzanine card?
A. drivers
B. devices
C. devtree
D. info io
Answer: D
Explanation:
http://h20000.www2.hp.com/bizsupport/TechSupport/Document.jsp?prodSeriesId=447681&objectI D=c00273667
NEW QUESTION: 3
All of the following statements are true regarding conflict management EXCEPT:
A. Ground rules and group norms reduce the amount of conflict
B. Source of conflict include scarce resources, scheduling priorities, and personal work style.
C. Conflict management is a tool/ techniques of Develop Project Team process
D. Conflicts should be addressed early and usually in private using a direct, collaborative approach.
E. When managed properly, conflicts can be healthy and lead to increased creativity
Answer: C
NEW QUESTION: 4
Fill in the blank in the following sentence:
Principal component analysis (PCA) is a statistical tool to decompose a ____________ matrix into its principal components and is useful in risk management to reduce dimensions.
A. Positive semi-definite
B. Correlation
C. Volatility
D. Covariance
Answer: A
Explanation:
Explanation
PCA is a statistical tool that decomposes a positive semi-definite matrix into its principal components. The first few principal components explain nearly all the variation and other components can then be ignored as they are too small in the larger picture. PCA in risk management is applied to a positive semi-definite correlation or covariance matrix to reveal the principal components that cause the variation. By allowing a focus on a few components, PCA reduces dimensionality.
While performing the math of PCA is unlikely to be asked in the PRMIA exam, you should remember that principal components have the additional property of being uncorrelated to each other which makes it useful as it is possible to vary one of the components without having to worry about the effect of that on the other components.