Google udemy selected Professional-data-engineer Google Cloud Certified Questions Answers by Stevie q188 vce pdf

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Exam Name:	Google Professional Data Engineer Exam
Exam Code:	Professional-Data-Engineer Dumps
Vendor:	Google	Certification:	Google Cloud Certified
Questions:	330 Q&A's	Shared By:	stevie

Question 32

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

Discussion

Question 33

Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

Options:

Run a local version of Jupiter on the laptop.

Grant the user access to Google Cloud Shell.

Host a visualization tool on a VM on Google Compute Engine.

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

Discussion

Question 34

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.

Discussion

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Question 35

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

Options:

Redefine the schema by evenly distributing reads and writes across the row space of the table.

The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Discussion

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