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NEW QUESTION: 1
What are features of optical recording media? (Select two.)
A. It uses holographic properties of glass crystals to store patterns.
B. Optical properties can be changed by a laser.
C. It relies on heat properties to burn data onto media.
D. It is commonly used as the distribution media for software.
E. It can be used only for WORM drives.
Answer: B,D

NEW QUESTION: 2
Given the code fragment:

Which three are true?
A. On line 3, the t1 thread stops and waits until the current thread finishes.
B. This code cannot throw a checked exception.
C. On line 4, the t1 thread is dead.
D. On line 3, the current thread stops and waits until the t1 thread finishes.
E. This code may throw a checked exception.
F. On line 4, the t1 thread is waiting to run.
Answer: C,D,E
Explanation:
Thejoin()methods waits for this thread to die.

NEW QUESTION: 3
A Symmetrix VMAX 20K customer is planning to use virtual provisioning encapsulation to encapsulate LUNs from an external array. What objects are created when using SYMCLI to perform the encapsulation?
A. eDisk and external disk group
B. External disk group, data device and thin device
C. eDisk and Symmetrix device
D. eDisk, data device and thin device
Answer: D

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