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NEW QUESTION: 1
You have the following class definition:

You need to create an extension class and create a new static method in it that converts miles to kilometers and then call the method from another class.
How should you complete the code and call the method? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation
Box 1: final class CustUnitConv_Extension
Box 2: var km = UnitConv::miToKm(62.1371);
Reference:
https://docs.microsoft.com/en-us/dynamics365/fin-ops-core/dev-itpro/extensibility/method-wrapping-coc

NEW QUESTION: 2
What are the two guiding principles of accrual accounting?
A. Revenue recognition and measurement
B. Cost and materiality
C. Revenue recognition and matching
D. Expense recognition and matching
Answer: C

NEW QUESTION: 3
Refer to the exhibit.

A cloud engineer is replicating a FlexPod design. The current design has a Cisco UCS B-Series Chassis
with six Cisco B200 M2 blades that connect to a NetApp FAS800 storage array. VSAN 10 is configured on
the Cisco Nexus 5000 Series Switch to handle all storage traffic. VMs on the Cisco B200 M2 blades can
read and write to NAS on the NetApp FAS8000. The engineer wants to connect a second Cisco UCS B-
Series Chassis with four Cisco B200 M4 blades to the NetApp array. The engineer has configured four
Fibre Channel uplink ports from the Cisco UCS 6248 UP Fabric Interconnect to the Cisco Nexus Fibre
Channel ports fc1/23 and fc 1/24. However, the Fibre Channel ports on the Cisco UCS 6248 fail to initialize
after the engineer enables the port.
Which reason for the problem is most likely true?
A. The VSAN under port properties is incorrect.
B. The port negotiation speed is set to 8 Gbps.
C. The switch port mode on the Cisco Fabric Extender Fibre Channel port is set to NP.
D. The SFP type is 10 Gigabit Ethernet instead of 4 Gigabit Ethernet.
E. The Fibre channel port is set to uplink instead of Fibre Channel storage port.
Answer: C

NEW QUESTION: 4
You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.
You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.
You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.
Which three actions must you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.
B. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to loss.
C. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.
D. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.
E. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to minimize.
F. Add code to the bird_classifier_train.py script to calculate the validation loss of the model and log it as a float value with the key loss.
Answer: A,C,D
Explanation:
Explanation
AD:
primary_metric_name="accuracy",
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE
Optimize the runs to maximize "accuracy". Make sure to log this value in your training script.
Note:
primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script.
primary_metric_goal: It can be either PrimaryMetricGoal.MAXIMIZE or PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.
F: The training script calculates the val_accuracy and logs it as "accuracy", which is used as the primary metric.