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NEW QUESTION: 1
You need to ensure that an incomplete sales document CANNOT be saved. What must you do?
A. Assign an incompleteness procedure to the respective item categories of the document
B. Select the Dialog Message for Incompletion flag in the respective schedule line categories
C. Assign an incompleteness procedure to the respective schedule line categories of the document
D. Select the Dialog Message for Incompletion flag in the respective sales document type
Answer: D

NEW QUESTION: 2
A user has requested a device that could be used for web-conferences while away from work. The device will mainly be used for emailing, document reviewing, and phone calls.
Which of the following mobile devices would a technician MOST likely recommend?
A. Web camera
B. Tablet
C. Phablet
D. Smart camera
Answer: C

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