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NEW QUESTION: 1
You have a computer that runs Windows 10.
You need to ensure that the next time the computer restarts, the computer starts in safe mode.
What should you use?
A. System in Control Panel
B. the Bcdboot command
C. the Restore-Computer cmdlet
D. the System Configuration utility
Answer: D
Explanation:
Explanation/Reference:
Explanation:
References: http://www.digitalcitizen.life/4-ways-boot-safe-mode-windows-10

NEW QUESTION: 2
You have a multi-tier application comprised of a web, database, and application servers. Which product will allow you to move the Apache web server to the public cloud while maintaining the same network settings and connectivity with the other two servers?
A. vCloud Networking & Security
B. vFabric Suite
C. vCloud Connector
D. vCenter
Answer: C
Explanation:
Reference:http://download3.vmware.com/vcloud_assets/Doc/PDF/MovingCloudWorkloadsvCC.pdf

NEW QUESTION: 3
You need to implement early stopping criteria as suited in the model training requirements.
Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

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