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NEW QUESTION: 1
A. Option C
B. Option B
C. Option D
D. Option A
Answer: C
Explanation:
- Scenario: Database Issues The current database does not perform well. Additionally, a recent disk problem caused the system to go down, resulting in lost sales revenue. In reviewing the current system, you found that there are no automated maintenance procedures. The database is severely fragmented, and everyone has read and write access.
- After the degree of fragmentation is known, use the following table to determine the best method to correct the fragmentation.
NEW QUESTION: 2
When companies come together to work in an integrated manner such as extranets, special care must be taken to ensure that each party promises to provide the necessary level of protection, liability and responsibility. These aspects should be defined in the contracts that each party signs. What describes this type of liability?
A. Cascade liabilities
B. Down-flow liabilities
C. Down-set liabilities
D. Downstream liabilities
Answer: D
Explanation:
Explanation/Reference:
Explanation:
References: HARRIS, Shon, All-In-One CISSP Certification Exam Guide, McGrawHill/Osborne, 2001, Page 659.
NEW QUESTION: 3
What happens when you attempt to compile and run the following code?
#include <list>
#include <iostream>
using namespace std;
template<class T>
void print(T start, T end) {
while (start != end) {
std::cout << *start << " "; start++;
}
}
int main()
{
int t1[] ={ 1, 2, 3, 4, 5};
list<int> l1(t1, t1 + 5);
l1.remove(2);
print(l1.begin(), l1.end()); cout<<endl;
return 0;
}
A. program outputs: 1 3 4 5
B. program outputs: 1 2 4 5
C. program outputs: 3 4 5
D. program outputs: 4 5
Answer: A
NEW QUESTION: 4
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote