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NEW QUESTION: 1
次のコード・フラグメントがあります:

結果は何ですか。
A. A Work done
B. コンパイルが失敗します。
C. A B C Work done
D. A B C D Work done
Answer: D
Explanation:

NEW QUESTION: 2
Which technology can you implement to centrally mitigate potential threats when users on your network download files that might be malicious?
A. Implement URL filtering on the perimeter firewall.
B. Verify that antivirus software is installed and up to date for all users on your network.
C. Enable file-reputation services to inspect all files that traverse the company network and block files with low reputation scores.
D. Verify that the company IPS blocks all known malicious websites.
Answer: A

NEW QUESTION: 3

A. Option C
B. Option A
C. Option B
D. Option D
Answer: B,C
Explanation:
Reference: https://www.paloaltonetworks.com/documentation/80/pan-os/pan-os/app-id/use- application-objects-in-policy/create-a- custom-application

NEW QUESTION: 4
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