Free Google Professional-Data-Engineer Exam Questions

Question 1

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

A : ange the processing job to use Google Cloud Dataproc instead

B : nually start the Cloud Dataflow job each morning when you get into the office.

C : eate a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

D : nfigure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Answer: C

Question 2

You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO.

You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in

Project A. The Cloud SQL instance is running jn Project B and does not have a public IP address. After

deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to

connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects.

You want to resolve this error while ensuring that the data does not go through the public internet. What

should you do?

A : Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peeredsubnet range to access all instances on the network.

B : Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.

C : Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.

D : Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.

Answer: C

Question 3

You are part of a healthcare organization where data is organized and managed by respective data owners in

various storage services. As a result of this decentralized ecosystem, discovering and managing data has

become difficult You need to quickly identify and implement a cost-optimized solution to assist your

organization with the following

• Data management and discovery

• Data lineage tracking

• Data quality validation

How should you build the solution?

A : Use BigLake to convert the current solution into a data lake architecture.

B : Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking

C : Use BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation

D : Use Dataplex to manage data, track data lineage, and perform data quality validation.

Answer: D

Question 4

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud

Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a

Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to

run additional transformation jobs in BigQuery The transformation jobs are different for every table These

jobs might take hours to complete You need to determine the most efficient and maintainable workflow to

process hundreds of tables and provide the freshest data to your end users. What should you do?

A : 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage. Dataproc. and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline3 Schedule the DAG to run hourly

B : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators.2 Create a separate DAG for each table that needs to go through the pipeline3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

C : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage, Dataproc. and BigQuery operators2 Create a separate DAG for each table that needs to go through the pipeline3 Schedule the DAGs to run hourly

D : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline.3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

Answer: B

Question 5

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants. What should you do?

A : crease the size of the dataset by collecting additional data.

B : ain a linear regression to predict a credit default risk score.

C : move the bias from the data and collect applications that have been declined loans.

D : tch loan applicants with their social profiles to enable feature engineering.

Answer: B