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NEW QUESTION: 1
U+ Bank has configured Next-Best-Action designer to display marketing offers to its customers on the U+ Bank website.
The bank would like to display different offers to different customers based on relevancy and eligibility defined by the business. The Strategy Designer has decided to use the "Offer targeting using audiences and AI" method to implement this requirement.
Which three tasks need to be performed to implement this requirement? (Choose Three)
A. Configure a prioritization strategy
B. Configure an alternate strategy
C. Configure an audience
D. Configure conditions for offer eligibility
E. Select a relevancy strategy
Answer: A,B,D

NEW QUESTION: 2
A new solution is created with Iwo tables, Client and Contracts. The only modification made on the Relationships Graph is to establish a relationship between them which is set to delete related records in Contracts when a record in Client is deleted.

What happens when the Client table occurrence is deleted from the Relationships Graph?
A. Any records in the Contracts table that were related to matching Client records will have values in the Client ID field deleted.
B. The formula in the calculation field Name Full (defined as Name First & " & Name Last) will be broken.
C. Since it is the only occurrence of the Client table on the graph, it cannot be deleted.
D. Any records in the Contracts table that were related to matching Client records are deleted.
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 multi-hot column
B. Create a hash bucket
C. Create an embedding column
D. Create a one-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