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NEW QUESTION: 1
Answer:
Explanation:
Explanation
From Scenario: Permissions must be assigned by using Role Based Access Control (RBAC).
The following cmdlet is used to sign-in to Azure: Add-AzureAccount
If necessary, the following Azure cmdlets can be used to select the desired subscription:
Get-AzureSubscription
Select-AzureSubscription -SubscriptionName "SomeSubscription"
Set-AzureSubscription -SubscriptionName "SomeSubscription " `
References:
https://blogs.msdn.microsoft.com/cloud_solution_architect/2015/05/14/using-a-service-principal-for-azure-powe
NEW QUESTION: 2
A. Option C
B. Option D
C. Option A
D. Option B
Answer: A
NEW QUESTION: 3
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
A. [1, 0, 0, 0, 0, 0, 0]
B. [0, 0, 0, 1, 0, 0, 1]
C. [0, 1]
D. [0, 5, 0, 0, 0, 0]
Answer: A,C
Explanation:
Explanation
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent
'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations
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