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NEW QUESTION: 1
You are designing an IPv4 any source multicast redundancy solution. Which technology ensures the quickest RP convergence?
A. Embedded RP
B. MSDP anycast RP
C. Auto-RP
D. bootstrap router
Answer: B

NEW QUESTION: 2
You plan to run an automatic update on the ABC classifications of all items.
You need to identify which ABC code to update based on the sales of eachitem in a given period.
Which ABC code should you identify?
A. Carrying cost
B. Margin
C. Value
D. Revenue
Answer: D
Explanation:
BC code for revenue
A classifier that names categories in an ABC classification that ranks discrete categories of
items according to their revenue.
Note:ABC classification
A system for ranking discrete categories of items according to Pareto's 80/20 principle that
states that a small percentage of items account for the largest fraction of significance.
Incorrect:
Not Value, Not Margin: not such ABC codes.
Not D:ABC code for carrying cost classification
A classifier that names categories in an ABC classification that ranks discrete categories of
items according to their carrying cost.

NEW QUESTION: 3
モデルトレーニング要件に適した早期停止基準を実装する必要があります。
ソリューションの開発に使用する必要がある3つのコードセグメントはどれですか？回答するには、適切なコードセグメントをコードセグメントのリストから回答領域に移動し、正しい順序で並べます。
注：回答の選択肢の複数の順序が正しいです。選択した正しい注文のいずれかのクレジットを受け取ります。

Answer:
Explanation:

Explanation:
You need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.
Example:
from azureml.train.hyperdrive import TruncationSelectionPolicy
early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5) Incorrect Answers:
Bandit is a termination policy based on slack factor/slack amount and evaluation interval. The policy early terminates any runs where the primary metric is not within the specified slack factor / slack amount with respect to the best performing training run.
Example:
from azureml.train.hyperdrive import BanditPolicy
early_termination_policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5 References:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters