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NEW QUESTION: 1
Which of the following are valid approaches to calculating potential future exposure (PFE) for counterparty risk:
I. Add a percentage of the notional to the mark-to-market value
II. Monte Carlo simulation
III. Maximum Likelihood Estimation
IV. Parametric Estimation
A. I, III and IV
B. All of the able
C. III and IV
D. I and II
Answer: D
Explanation:
Explanation
When a derivative position is entered into, its mark-to-market value is generally close to zero (though the notional may be high). With the passage of time, the derivative's value fluctuates in an unpredictable way, creating a counterparty exposure that may be difficult to estimate and risk manage. Counterparty risk in such cases is estimated based on Potential Future Exposure, which may be calculated using either:
a) Take the mark-to-market at present, and add a certain percentage of the notional, or b) Perform a Monte Carlo simulation, capturing the stochastic nature of the PFE.
Therefore I and II are valid choices. MLE and parametric estimation are not methods for calculating PFE.

NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Scale and Reduce sampling mode.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION: 3
You have an Azure SQL database that contains a table named Employee. Employee contains sensitive data in a decimal (10,2) column named Salary.
You need to ensure that nonprivileged users can view the table data, but Salary must display a number from 0 to 100.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: SELECT
Users with SELECT permission on a table can view the table data. Columns that are defined as masked, will display the masked data.
Incorrect:
Grant the UNMASK permission to a user to enable them to retrieve unmasked data from the columns for which masking is defined.
The CONTROL permission on the database includes both the ALTER ANY MASK and UNMASK permission.
Box 2: Random number
Random number: Masking method, which generates a random number according to the selected boundaries and actual data types. If the designated boundaries are equal, then the masking function is a constant number.

NEW QUESTION: 4
View the Exhibit and examine the structure of the CUSTOMERS table.

Using the CUSTOMERS table, you must generate a report that displays a credit limit increase of 15% for all customers.
Customers with no credit limit should have "Not Available" displayed.
Which SQL statement would produce the required result?
A. SELECT NVL(TO_CHAR(cust_credit_limit*.15),'Not Available') "NEW CREDIT"FROM customers;
B. SELECT TO_CHAR(NVL(cust_credit_limit*.15,'Not Available')) "NEW CREDIT"FROM customers;
C. SELECT NVL(cust_credit_limit,'Not Available')*.15 "NEW CREDIT"FROM customers;
D. SELECT NVL(cust_credit_limit*.15,'Not Available') "NEW CREDIT"FROM customers;
Answer: A