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NEW QUESTION: 1




Answer:
Explanation:
Explanation

NEW QUESTION: 2
A staff auditor, nearly finished with an audit engagement, discovers that the director of marketing has a gambling habit. The gambling issue is not directly related to the existing engagement, and there is pressure to complete the current engagement. The auditor notes the problem and forwards the information to the chief audit executive, but performs no further follow-up.
Which of the following statements is true about the auditor's actions?
A. They are not in violation of the Standards but are in violation of the IIA Code of Ethics.
B. They are in violation of the IIA Code of Ethics because the auditor withheld meaningful information.
C. They are in violation of neither the IIA Code of Ethics nor the Standards.
D. They are in violation of the Standards because the auditor did not properly follow up on a red flag that might indicate the existence of fraud.
Answer: C

NEW QUESTION: 3
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself.
Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters