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NEW QUESTION: 1
A customer has two Power 570 servers that need to be replaced to accommodate company growth.
* The first server runs a transactional workload with high server usage during the day (8am-8pm)
* The second server runs analytic queries based on the transactional data during the night (10pm-6am).
A technical sizing analysis determined that each server would require (18) POWERS
3 .52GHz cores and 160GB RAM, considering the needed growth.
What would be the most cost-efficient way to satisfy the customer needs?
A. (1) new Power 770 with 36 active cores and 384GB active, virtualized with capped
LPARs
B. (1) new Power S824 with 24 cores and 384GB and DLPAR CPU resources among
LPARs
C. (1) new Power S824 with 36 cores and 384GB, virtualized with capped LPARs.
D. (2) new Power S824 with 20 cores and 192GB RAM each.
Answer: A

NEW QUESTION: 2
Which item represents a method of increasing storage utilization efficiency?
A. transparent provisioning
B. thick provisioning
C. weighted provisioning
D. thin provisioning
Answer: D

NEW QUESTION: 3

A. Option A
B. Option B
C. Option D
D. Option C
Answer: A

NEW QUESTION: 4
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a hash bucket
B. Create a one-hot column
C. Create an embedding column
D. Create a multi-hot column
Answer: C
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- deep-model.html