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NEW QUESTION: 1
You have a Power BI Desktop project that uses DirectQuery to access an on-premises Microsoft SQL Server database.
From Power BI Desktop, you can query the database.
When you publish the Power BI Desktop project to the Power BI service, the visualizations cannot display the data.
What should you do to resolve the issue?
A. Locate the published dataset for the project in the Power BI service and configure the data source credentials.
B. Install the on-premises data gateway and configure a data source.
C. Configure a Microsoft Azure ExpressRoute connection between the on-premises network and the Power BI service.
D. Install the on-premises data gateway (personal mode) and republish the project.
Answer: B
Explanation:
Explanation/Reference:
References:
https://docs.microsoft.com/en-us/power-bi/service-gateway-sql-tutorial

NEW QUESTION: 2

A)

B)

C)

D)

E)

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

NEW QUESTION: 3
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create an embedding column
B. Create a multi-hot column
C. Create a one-hot column
D. Create a hash bucket
Answer: A
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