Complete Professional-Data-Engineer Google Materials

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Question 8

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

Options:

Linear regression

Logistic classification

Recurrent neural network

Feedforward neural network

Question 9

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

Options:

Eliminate features that are highly correlated to the output labels.

Combine highly co-dependent features into one representative feature.

Instead of feeding in each feature individually, average their values in batches of 3.

Remove the features that have null values for more than 50% of the training records.

Question 10

Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Options:

Threading

Serialization

Dropout Methods

Dimensionality Reduction

Question 11

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?