Free Google Professional-Data-Engineer Exam Questions

Question 1

You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be

launched with their latest available configurations from a container registry. Your GKE nodes need to have

GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure

and manage the deployment process. What should you do?

A : Use Compute Engi.no startup scriots to pull container Images, and use gloud commands to provision theinfrastructure.

B : Use GKE to autoscale containers, and use gloud commands to provision the infrastructure.

C : Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.

D : Use Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.

Answer: C

Question 2

An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?

A : Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.

B : a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.

C : ite an application that makes a queue in a NoSQL database

D : ite an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic

Answer: C

Question 3

Your company's customer_order table in BigOuery stores the order history for 10 million customers, with a

table size of 10 PB. You need to create a dashboard for the support team to view the order history. The

dashboard has two filters, countryname and username. Both are string data types in the BigQuery table. When

a filter is applied, the dashboard fetches the order history from the table and displays the query results.

However, the dashboard is slow to show the results when applying the filters to the following query:

How should you redesign the BigQuery table to support faster access?

A : Cluster the table by country field, and partition by username field.

B : Partition the table by country and username fields.

C : Cluster the table by country and username fields

D : Partition the table by _PARTITIONTIME.

Answer: C

Question 4

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A : -create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

B : commend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

C : dify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

D : ite an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: C

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