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NEW QUESTION: 1
Refer to the exhibit.
How should a Synergy integrator pair interconnect bays to set up redundant fabrics in an HPE Synergy 12000 Frame?
A. Interconnect 1 bays 1 and 4: bays 2 and 5: bays 3 and 6.
B. Interconnect bays 1 and 3: bays 2 and 4: bays 5 and 6.
C. Interconnect bays 1 and 1 and 6: bays 2 and 5: bays 3 and 4.
D. Interconnect bays 1 and bays 2: bays 3 and 4: bays 5 and 6.
Answer: C
NEW QUESTION: 2
MySQL Enterprise透過的データ暗号化（TDE）について正しいことは何ですか？
A. テーブルスペースキーは、バックグラウンドで手動で管理されます。
B. 暗号化キーは一元化されたキーメモリの場所に保存
C. データベースの物理ファイルを暗号化することにより、保存データの暗号化を有効にします。
D. データは、ストレージに書き込まれた後、リアルタイムで自動的に暗号化されます。
Answer: C
NEW QUESTION: 3
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Number of nodes in each hidden layer
B. Biases
C. Weights
D. Number of hidden layers
Answer: A,D
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
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