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NEW QUESTION: 1
Ein Team hat verschiedene Ideen, wie ein Merkmal eines im Bau befindlichen Produkts behandelt werden kann. Obwohl der Sprint vor zwei Tagen begann, haben sie immer noch keinen Konsens erreicht. Was soll der Projektmanager tun?
A. Bitten Sie den Product Owner, der der wichtigste Stakeholder ist, zwischen den verschiedenen Ideen zu wählen.
B. Geben Sie eine Meinung zu den Vor- und Nachteilen jeder Idee ab und versuchen Sie, in einem erleichterten Meeting einen Konsens zu erzielen
C. Schlagen Sie der Gruppe Ideen vor, bitten Sie alle, abzustimmen, und wählen Sie dann die beliebteste Idee aus
D. Planen Sie eine Sitzung, um allen die Möglichkeit zu geben, ihre Meinung zu jeder Idee zu äußern, und stimmen Sie dann ab und entscheiden Sie.
Answer: D
NEW QUESTION: 2
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 one-hot column
C. Create a multi-hot column
D. Create a hash bucket
Answer: A
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: 3
A. Option B
B. Option A
C. Option D
D. Option C
Answer: C,D
NEW QUESTION: 4
You are developing an Integration component that uses customer data. The source system defines customer data in a different format than expected. Which of the following options best describes how you would develop the component?
A. Create an object representation of customer data and use itin the component.
B. Write data from the source system into a database and read it back in the expected format.
C. The data formats are different, so it is not possible to develop the component.
D. Externalize the data transformation by mapping the source data format to a canonical data format.
Answer: A
Explanation:
Note: It is quite common to encounter use cases that require transformation of information from one format to another, especially in the area of enterprise integration. Source systems and target systems may use very different representations of data and in some cases, a canonical data model might be used as a common intermediate format. In some cases, the transformation is a simple field-to-field mapping whereas in other cases it is a complex manipulation and conversion of data. It should be possible to visually map the source and target representations with the ability to enrich the elements to support both simple and complex data transformations.