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NEW QUESTION: 1
Calculate the 99% 1-day Value at Risk of a portfolio worth $10m with expected returns of 10% annually and volatility of 20%.
A. 0
B. 1
C. 2
D. 3
Answer: B
Explanation:
Explanation
Be wary of questions asking you to calculate VaR where the mean or expected returns are different from zero.
The VaR formula of z-value times standard deviation needs to have an adjustment for the expected return [ie use VaR = z-value times standard deviation minus expected return]. In this case, the standard deviation for 1 day for the portfolio is =SQRT(1/250)*20%*$10m = $126,491. The VaR is therefore (2.326 * $126,491) - ($10,000,000 * 10% * 1/250) = $290,218.

NEW QUESTION: 2
Which memory capacity does the BL460c Gen8 support?
A. 16 DIMM slots supporting 512GB
B. 6 DIMM slots supporting 512MB
C. 4 DIMM slots supporting 256MB
D. 8 DIMM slots supporting 256MB
Answer: A
Explanation:
Reference: http://www8.hp.com/us/en/products/proliant-servers/productdetail.html?oid=5177949#!tab%3Dspecs (memory)

NEW QUESTION: 3
Which statements are true about BFD? (Select two.)
A. BFD can be enabled either globally or per interface.
B. BFD provides very fast link error detection but uses more resources than routing protocol fast hellos.
C. BFD not only detects link failures but also speeds up routing event propagation between neighbors by using fast hellos.
D. BFD is routing protocol independent and can work with all modern routing protocols.
E. BFD is resource efficient, as some function can be supported directly in the hardware.
F. BFD can be configured only over Ethernet links.
Answer: D,E

NEW QUESTION: 4
You need to set up the Permutation Feature Importance module according to the model training requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Accuracy
Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
Box 2: R-Squared

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