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NEW QUESTION: 1
Software Defined Storage (SDS) uses which type of technology to function?
A. Application
B. authentication
C. virtualization
D. replication
Answer: C

NEW QUESTION: 2
展示を参照してください。

PE3に新しいVRFを作成しました。デバッグを有効にしました
PE 1のip bgp vpnv4ユニキャストアップデート。ルートはデバッグで確認できますが、BGP VPNv4テーブルでは確認できません。どちらが正しいですか？ （2つ選択してください）
A. address-family ipv4 vrfがPE3で構成されていません
B. PE1は自動ルートフィルタリングによりルートを拒否します
C. VPNv4はPE1とPE3の間に構成されていません
D. PE1のVRFにルートターゲットインポート999：999を構成すると、ルートが受け入れられます
E. PE3のVRFにルートターゲットインポート999：999を構成すると、ルートが受け入れられます
Answer: B,D
Explanation:
Explanation
Because some PE routers might receive routing information they do not require, a basic requirement is to be able to filter the MP-iBGP updates at the ingress to the PE router so that the router does not need to keep this information in memory.
The Automatic Route Filtering feature fulfills this filtering requirement. This feature is available by default on all PE routers, and no additional configuration is necessary to enable it. Its function is to filter automatically VPN-IPv4 routes that contain a route target extended community that does not match any of the PE's configured VRFs. This effectively discards any unwanted VPN-IPv4 routes silently, thus reducing the amount of information that the PE has to store in memory -> Answer 'PE1 will reject the route due to automatic route filtering' is correct.

NEW QUESTION: 3
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Number of nodes in each hidden layer
B. Weights
C. Biases
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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