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NEW QUESTION: 1
Ein Benutzer fordert einen neuen Laptop-Akku an, der eine längere Laufzeit bietet. Auf der aktuellen Batterie des Benutzers sind die folgenden Informationen aufgedruckt:
14,8 V 6-Zellen-Lithium-Ionen-Akku mit 1800 mAh
Welche der folgenden Akkuspezifikationen führt zu einer längeren Laufzeit, ist aber dennoch mit dem aktuellen Laptop kompatibel?
A. 19,2 V 6-Zellen-Lithium-Ionen-Akku mit 1800 mAh
B. 14,8 V 6-Zellen-Lithium-Tonnen-Batterie mit 2200 mAh
C. 14,8 V 6-Zellen-Nickel-Cadmium-Batterie mit 1800 mAh
D. 14,8 V 9-Zellen-Lithium-Ionen-Akku mit 1800 mAh
Answer: D
NEW QUESTION: 2
Which statement is true about an active object?
A. It is dependent on the overall system execution.
B. It is an independent object that can communicate with other active objects asynchronously.
C. It does not contain state.
D. It is unstructured to allow the class to be manipulated easily.
Answer: B
NEW QUESTION: 3
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a hash bucket
B. Create an embedding column
C. Create a multi-hot column
D. Create a one-hot column
Answer: B
Explanation:
Explanation
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference:
https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-dee
NEW QUESTION: 4
Scenario: Client connections to certain virtual servers are abnormally high. A Citrix Administrator needs to be alerted whenever the connections pass a certain threshold.
How can the administrator use Citrix ADC Management and Analytics System (MAS) to accomplish this requirement?
A. Configure specific alerts for virtual servers using Citrix ADC MAS.
B. Configure TCP Insight on the Citrix ADC MAS.
C. Configure SMTP Reporting on the Citrix ADC MAS by adding the threshold and email address.
D. Configure Network Reporting on the Citrix ADC MAS by setting the threshold and email address.
Answer: D
Explanation:
https://docs.citrix.com/en-us/netscaler-mas/12/network-reporting.html