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Exam Code: Analytics-Con-301 Dumps

Questions and Answers: 104

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NEW QUESTION: 1
What important troubleshooting information can be gathered for the Video Accelerator from the WAE#show stat accelerator video?
A. Whether or not the WAE(Config)#accelerator video enable command has been issued
B. The license status of the Video Accelerator
C. The total Non-Windows Media and Non-Live Video Streams
D. Troubleshooting of the Video Accelerator can only be done through the Central Manager GUI
Answer: C

NEW QUESTION: 2

A. Option B
B. Option D
C. Option A
D. Option C
Answer: C
Explanation:
Rapid Elasticity Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time. Consumers can leverage rapid elasticity of the cloud when they have a fluctuation in their IT resource requirements. For example, an organization might require doubling the number of web and application servers for a specific duration to accomplish a specific task. For the remaining period, they might want to release idle server resources to cut down the expenses. The cloud enables consumers to grow and shrink the demand for resources dynamically. EMC E10-001 Student Resource Guide. Module 13: Cloud Computing

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 a one-hot column
C. Create an embedding column
D. Create a multi-hot column
Answer: C
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
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You have deployed several GS-series virtual machines (VMs) in Microsoft Azure. You plan to deploy Microsoft SQL Server in an Always On Availability Group. You expect to have less than 1 million IO transaction per month.
You need to recommend a storage solution for the SQL Servers. The solution must minimize costs.
Which storage option should you use?
A. Premium P10 disk storage
B. Standard geo-redundant disk storage
C. Standard geo-redundant blob storage
D. Premium P30 disk storage
E. Premium P20 disk storage
F. Standard zone redundant blob storage
G. Standard locally redundant blob storage
H. Standard locally redundant disk storage
Answer: A
Explanation:
P10 has 500 IOPS per disk, which provides for more than 1 million IOPS per month.
Note: 3600*30* 500 is 54 million IOPS/month.
References:
https://azure.microsoft.com/en-us/pricing/details/managed-disks/


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