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NEW QUESTION: 1
Which of the following standards primarily pertains to cabling designs and setups in a data center?
A. BICSI
B. IDCA
C. Uptime Institute
D. NFPA
Answer: A
Explanation:
Explanation
The standards put out by Building Industry Consulting Service International (BICSI) primarily cover complex cabling designs and setups for data centers, but also include specifications on power, energy efficiency, and hot/cold aisle setups.

NEW QUESTION: 2
A technician has traced a POTS line to a 110 block. Which of the following devices would the technician MOST likely use to see if the line carries a dial tone?
A. Protocol Analyzer
B. Toner Probe
C. Butt Set
D. Punch Down Tool
Answer: C

NEW QUESTION: 3
Which of the following is the MAIN benefit of using data analytics when testing the effectiveness of controls?
A. The full population can be tested.
B. Analytics remove the need to focus on areas of higher risk
C. Analytics can be applied to any type of control
D. The demand for IS auditors is reduced over time
Answer: A

NEW QUESTION: 4
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

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