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NEW QUESTION: 1
A customer has multiple related applications. The replicated LUNs for these applications must be consistent with each other. The customer experiences a link failure.
Which two methods will meet the customer's requirement? (Choose two.)
A. Create a single consistency group for all LUNs.
B. Use log shipping to enable roll forward to become consistent.
C. Create multiple consistency groups.
D. Use Hitachi Universal Replicator with journals to ensure consistency.
Answer: A,D

NEW QUESTION: 2
The implementations group has been using the test bed to do a 'proof-of-concept' that requires both Client 1 and
Client 2 to access the WEB Server at 209.65.200.241. After several changes to the network addressing, routing scheme, DHCP services, NTP services, layer 2 connectivity, FHRP services, and device security, a trouble ticket has been opened indicating that Client 1 cannot ping the 209.65.200.241 address.
Use the supported commands to isolated the cause of this fault and answer the following questions.
On which device is the fault condition located?
A. R2
B. R3
C. R4
D. R1
E. ASW1
F. DSW2
G. ASW2
H. DSW1
Answer: D
Explanation:
On R1, for IPV4 authentication of OSPF the command is missing and required to configure------ ip ospf authentication message-digest

NEW QUESTION: 3
An Approval Process begins when a record is:
A. Created
B. Submitted for Approval
C. Saved
D. Edited
Answer: B

NEW QUESTION: 4
Azure Machine Learningのハイパードライブ機能を使用して、モデルをトレーニングするときに最適なハイパーパラメーター値を決定することを計画しています。
次のハイパーパラメータ値の組み合わせを試すには、ハイパードライブを使用する必要があります。
* Learning_rate：0.001〜0.1の任意の値
* batch_size：16、32、または64
Hyperdrive実験の検索スペースを構成する必要があります。
どの2つのパラメータ式を使用する必要がありますか？それぞれの正解は、解決策の一部を示しています。
注：正しい選択はそれぞれ1ポイントの価値があります。
A. batch_sizeの通常の式
B. learning_rateの選択式
C. batch_sizeの選択式
D. batch_sizeの統一式
E. learning_rateの統一式
Answer: C,E
Explanation:
B: Continuous hyperparameters are specified as a distribution over a continuous range of values. Supported distributions include:
* uniform(low, high) - Returns a value uniformly distributed between low and high D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:
* one or more comma-separated values
* a range object
* any arbitrary list object
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

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