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NEW QUESTION: 1
John is a merchant. He has set up a LAN in his office. Some important files are deleted as a result of virus attack. John wants to ensure that it does not happen again. What will he use to protect his data from virus?
A. Antivirus
B. Firewall
C. Symmetric encryption
D. Backup
Answer: A

NEW QUESTION: 2
You need to configure retries in the LoadUserDetails function in the Database class without impacting user experience.
What code should you insert on line DB07?
To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Policy
RetryPolicy retry = Policy
Handle<HttpRequestException>()
Retry(3);
The above example will create a retry policy which will retry up to three times if an action fails with an exception handled by the Policy.
Box 2: WaitAndRetryAsync(3,i => TimeSpan.FromMilliseconds(100* Math.Pow(2,i-1))); A common retry strategy is exponential backoff: this allows for retries to be made initially quickly, but then at progressively longer intervals, to avoid hitting a subsystem with repeated frequent calls if the subsystem may be struggling.
Example:
Policy
Handle<SomeExceptionType>()
WaitAndRetry(3, retryAttempt =>
TimeSpan.FromSeconds(Math.Pow(2, retryAttempt))
);
References:
https://github.com/App-vNext/Polly/wiki/Retry

NEW QUESTION: 3

A. ip ospf authentication-key
B. ip ospf priority 1
C. neighbor 192.168.0.112 cost md5
D. ip ospf authentication message-digest
Answer: C

NEW QUESTION: 4
Why do you need to split a machine learning dataset into training data and test data?
A. So you can try two different sets of features
B. So you can use one dataset for a wide model and one for a deep model
C. To allow you to create unit tests in your code
D. To make sure your model is generalized for more than just the training data
Answer: D
Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/