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Google Professional-Data-Engineer Exam Sample Questions

Question # 1

Why do you need to split a machine learning dataset into training data and test data?

A. So you can try two different sets of features

B. To make sure your model is generalized for more than just the training data

C. To allow you to create unit tests in your code

D. So you can use one dataset for a wide model and one for a deep model

Answer

B.

To make sure your model is generalized for more than just the training data

The flaw with evaluating a predictive model on training data is that it does not inform you on how well the
model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset
rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test
dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training
dataset. This is called overfitting.

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Question # 2

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. Continuously retrain the model on just the new data.

B. Continuously retrain the model on a combination of existing data and the new data.

C. Train on the existing data while using the new data as your test set.

D. Train on the new data while using the existing data as your test set.

Answer

D.

Train on the new data while using the existing data as your test set.

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Question # 3

Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

A. Field promotion

B. Randomization

C. Salting

D. Hashing

Answer

A.

Field promotion

By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make
it easier to design a row key that facilitates queries

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Question # 4

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

A. Disable writes to certain tables.

B. Restrict access to tables by role.

C. Ensure that the data is encrypted at all times.

D. Restrict BigQuery API access to approved users.

E. Segregate data across multiple tables or databases.

Answer

B.

Restrict access to tables by role.

D.

Restrict BigQuery API access to approved users.

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Question # 5

Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use K-means Clustering to detect faces in the pixels.

B. Use feature engineering to add features for eyes, noses, and mouths to the input data.

C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer

C.

Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most
one hidden layer in between. More than three layers (including input and output) qualifies as “deep” learning.
So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer’s
output. The further you advance into the neural net, the more complex the features your nodes can recognize,
since they aggregate and recombine features from the
previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features
of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that
detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.

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