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NEW QUESTION: 1
Identify the benefit of Solution Manager: (Choose five)
A. Reduced Cost of Operations
B. More leverage for IT investments
C. Reduced Cost of Implementation
D. More reliable IT Solution
E. Faster ROI
F. Reduced ROI
Answer: A,B,C,D,E

NEW QUESTION: 2
DRAG DROP
Drag the step in the SCEP workflow on the left into the correct order of operation on the right.

Answer:
Explanation:

NEW QUESTION: 3
Which three prerequisites are needed to use the VMware Remote Console? (Choose three.)
A. VMware Tools installation ISO attached.
B. VMware Tools installed.
C. Guest Operating System installed.
D. Virtual machine is powered on.
E. Network IP Configured.
Answer: B,D,E

NEW QUESTION: 4
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