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NEW QUESTION: 1
Where can you view anti-spam status?
A. SmartDashboard
B. SmartView Tracker
C. SmartUpdate
D. SmartView Monitor
Answer: D

NEW QUESTION: 2
What are two effects of deploying an EPMA application? (Choose two.)
A. The application is sent out to a star schema for use by Essbase and other products.
B. The EPMA metadata is extracted to a text file.
C. The EPMA application metadata is used to create an FM application on the FM server.
D. When you redeploy an application, the application metadata is merged or replaced based on the changes made since the last deployment.
E. If enabled, it pushes the data to Essbase via Essbase Analytic Link for FM.
Answer: C,D

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