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NEW QUESTION: 1
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Weights
B. Number of nodes in each hidden layer
C. Number of hidden layers
D. Biases
Answer: B,C
Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all.
They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview

NEW QUESTION: 2
Which advantage is provided by using Active Directory as an external identity source?
A. It uses EAP chaining with EAP-FAST to authenticate users and computers.
B. It supports SAML for single sign-on.
C. It uses EAP chaining with EAP-TLS to authenticate users and computers.
D. It supports two factor-authentication using a PIN and a token.
Answer: A

NEW QUESTION: 3
An EMC customer has a three-node cluster with the Avamar Windows client installed. Each node has the Avamar Exchange 2007 plug-in installed. What should be done to avoid backing up the same data multiple times within a backup window?
A. Ensure both the physical and virtual nodes are in the same Avamar group
B. Exclude virtual node file systems from the physical node dataset
C. Ensure the Quorum drive is backed up only once per week instead of nightly
D. Verify that the Exchange database backup is not scheduled at the same time as the file system backup
Answer: B