Free Google Professional-Data-Engineer Exam Questions

Question 1

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A : eate a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

B : eate a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

C : eate a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

D : eate an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Answer: D

Question 2

You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm

To do this you need to add a synthetic feature. What should the value of that feature be?

A : 2+Y^2

B : 2

C : (X)

Answer: C

Question 3

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A : crease the CPU size on your server.

B : crease the size of the Google Persistent Disk on your server.

C : crease your network bandwidth from your datacenter to GCP.

D : crease your network bandwidth from Compute Engine to Cloud Storage.

Answer: C

Question 4

You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

A : Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.

B : Adopt multi-regional Cloud Storage buckets in your architecture.

C : Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.

D : Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.

Answer: D

Question 5

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