Online Access Free Google.Professional-Data-Engineer.v2025-12-07.q173 Practice Test (Page 16)

Professional-Data-Engineer Exam Question 71

Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

A.Migrate the workload to Google Cloud Dataflow

B.Use a higher-memory node so that the job runs faster

C.Use SSDs on the worker nodes so that the job can run faster

D.Use pre-emptible virtual machines (VMs) for the cluster

Professional-Data-Engineer Exam Question 72

By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A.Windows at every 100 MB of data

B.Single, Global Window

C.Windows at every 1 minute

D.Windows at every 10 minutes

Professional-Data-Engineer Exam Question 73

You are developing a new deep teaming model that predicts a customer's likelihood to buy on your ecommerce site. Alter running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the dat a. You want to improve the accuracy of the model when predicting new data. What should you do?

A.Increase the size of the training dataset, and increase the number of input features.

B.Increase the size of the training dataset, and decrease the number of input features.

C.Reduce the size of the training dataset, and increase the number of input features.

D.Reduce the size of the training dataset, and decrease the number of input features.

Professional-Data-Engineer Exam Question 74

Your startup has a web application that currently serves customers out of a single region in Asi a. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

A.Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.

B.Use a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US. Europe, and Asiareplication alter securing funding

C.Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.

D.Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.

Professional-Data-Engineer Exam Question 75

You need to modernize your existing on-premises data strategy. Your organization currently uses.
* Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.
* Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.
You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

A.Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.

B.Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.

C.Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

D.Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases.Orchestrate your pipelines with Cloud Composer..

Correct Answer: D

Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark workloads on Google Cloud. It is compatible with the open source ecosystem, so you can migrate your existing Hadoop clusters to Dataproc with minimal changes. Cloud Storage is a scalable, durable, and cost-effective object storage service that can replace HDFS for storing and accessing data. Cloud Storage offers interoperability with Hadoop through connectors, so you can use it as a data source or sink for your Dataproc jobs. Cloud Composer is a fully managed service that allowsyou to create, schedule, and monitor workflows using Apache Airflow. It is integrated with Google Cloud services, such as Dataproc, BigQuery, Dataflow, and Pub/Sub, so you can orchestrate your ETL pipelines across different platforms. Cloud Composer is compatible with your existing Airflow code, so you can migrate your existing orchestration processes to Cloud Composer with minimal changes.
The other options are not as suitable as Dataproc and Cloud Composer for this use case, because they either require more changes to your existing code, or do not meet your requirements. Dataflow is a fully managed service that allows you to create and run scalable data processing pipelines using Apache Beam. However, Dataflow is not compatible with your existing Hadoop code, so you would need to rewrite your ETL pipelines using Beam. Bigtable is a fully managed NoSQL database service that can handle large and complex data sets. However, Bigtable is not compatible with your existing Hadoop code, so you would need to rewrite your queries and applications using Bigtable APIs. Cloud Data Fusion is a fully managed service that allows you to visually design and deploy data integration pipelines using a graphical interface. However, Cloud Data Fusion is not compatible with your existing Airflow code, so you would need to recreate your orchestration processes using Cloud Data Fusion UI. References:
* Dataproc overview
* Cloud Storage connector for Hadoop
* Cloud Composer overview

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Professional-Data-Engineer Exam Question 71

Professional-Data-Engineer Exam Question 72

Professional-Data-Engineer Exam Question 73

Professional-Data-Engineer Exam Question 74

Professional-Data-Engineer Exam Question 75

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