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Google Professional-Data-Engineer Exam Sample Questions

Question # 1

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?

A. Eliminate features that are highly correlated to the output labels.

B. Combine highly co-dependent features into one representative feature.

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

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

Answer

B.

Combine highly co-dependent features into one representative feature.

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Question # 2

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Include multiple time series values within the row key

B. Keep the row keep as an 8 bit integer

C. Keep your row key reasonably short

D. Keep your row key as long as the field permits

Answer

C.

Keep your row key reasonably short

A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and
storage and increase the time it takes to get responses from the Cloud Bigtable server.

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Question # 3

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

A. Create a Google Cloud Dataflow job to process the data.

B. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

C. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

D. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

E. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks

Answer

A.

Create a Google Cloud Dataflow job to process the data.

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Question # 4

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

A. Send the data to Google Cloud Datastore and then export to BigQuery.

B. Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

C. Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

D. Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Answer

B.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud
Dataflow, and store the data in Google BigQuery.

---

Question # 5

e on a Cloud Dataproc cluster ____.

A. application node

B. conditional node

C. master node

D. worker node

Answer

C.

master node

The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m
suffix—for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".

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