New Google Cloud Certified Professional-Data-Engineer Exam Practice Questions

Total 370 Questions

Last Updated On: 20-Nov-2024

Question # 1

For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

Explanation
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance
for the best possible performance,
If it's not possible to create a instance in the same zone, you should create your instance in another zone within
the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create
your instance in us-central1-f. This change may result in several milliseconds of additional latency for each
Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from
your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable
request.

Question # 2

Which of these sources can you not load data into BigQuery from?
A. File upload
B. Google Drive
C. Google Cloud Storage
D. Google Cloud SQL

Google Cloud SQL

You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud
Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data
from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from
there.

Question # 3

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?
A. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
B. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
D. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table,
concatenates the FirstName value and LastName value for each user, and loads the
proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Question # 4

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.

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

Question # 5

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

Create a Google Cloud Dataflow job to process the data.

Question # 6

In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.
A. VPN connection
B. Special browser
C. SSH tunnel
D. FTP connection

SSH tunnel

Explanation
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.

Question # 7

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?
A. Hive
B. Pig
C. YARN
D. Spark

Hive

Pig

Spark

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