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NEW QUESTION: 1
Effective cash pooling can be described as? (Select one answer)
A. multi entity, multi option, multi level, multi currency
B. multi level, multi time zone, multi currency, multi system
C. multi time zone, multi user, multi currency, multi level
D. multi enterprise, multi user, multi currency, multi system
Answer: B
NEW QUESTION: 2
A. Option D
B. Option B
C. Option A
D. Option C
Answer: B
NEW QUESTION: 3
A developer has created a Visualforce page that uses a thirdparty JavaScript framework. The developer has decided to supply data to the JavaScript functions using JavaScript Remoting for Apex Controllers. What is the correct syntax to declare a remote method in Apex? Choose 2 answers
A. @Remoteobject ' global static String gettable
B. @RemoteAction global static String getTable()
C. @RemoteAction public static String getTable()
D. @RemoteAction global String getTable()
Answer: B,C
NEW QUESTION: 4
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