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Google Certified Professional Data Engineer Exam Sample Questions (Q267-Q272):

NEW QUESTION # 267
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 an embedding column
• C. Create a one-hot column
• D. Create a hash bucket

Answer: B 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 # 268
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street
Tim,553 Y street
Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

• A. ParDo
• B. Sink API
• C. Data extraction
• D. Source API

Answer: A Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do
NEW QUESTION # 269
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?

• A. Train on the new data while using the existing data as your test set.
• B. Continuously retrain the model on just the new data.
• C. Continuously retrain the model on a combination of existing data and the new data.
• D. Train on the existing data while using the new data as your test set.

Answer: A
NEW QUESTION # 270
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 an embedding column
• C. Create a one-hot column
• D. Create a hash bucket

Answer: B 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.
NEW QUESTION # 271
Which of these is NOT a way to customize the software on Dataproc cluster instances?

• A. Set initialization actions
• B. Log into the master node and make changes from there
• C. Modify configuration files using cluster properties
• D. Configure the cluster using Cloud Deployment Manager

Answer: D
NEW QUESTION # 272
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