Most Recent Google Professional-Data-Engineer Exam Questions & Answers

Prepare for the Google Cloud Certified Professional Data Engineer exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Google Professional-Data-Engineer exam and achieve success.

The questions for Professional-Data-Engineer were last updated on Jan 17, 2025.

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Question No. 1

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?

AUse BigLake to convert the current solution into a data lake architecture.

BBuild a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.

CUse BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation.

DUse Dataplex to manage data, track data lineage, and perform data quality validation.

Show Answer

Correct Answer: D

Dataplex is a Google Cloud service that provides a unified data fabric for data lakes and data warehouses. It enables data governance, management, and discovery across multiple data domains, zones, and assets. Dataplex also supports data lineage tracking, which shows the origin and transformation of data over time. Dataplex also integrates with Dataprep, a data preparation and quality tool that allows users to clean, enrich, and transform data using a visual interface. Dataprep can also monitor data quality and detect anomalies using machine learning. Therefore, Dataplex is the most suitable solution for the given scenario, as it meets all the requirements of data management and discovery, data lineage tracking, and data quality validation.Reference:

Dataplex overview

Automate data governance, extend your data fabric with Dataplex-BigLake integration

Dataprep documentation

Question No. 2

What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

Acreate a third instance and sync the data from the two storage types via batch jobs

Bexport the data from the existing instance and import the data into a new instance

Crun parallel instances where one is HDD and the other is SDD

Dthe selection is final and you must resume using the same storage type

Show Answer

Correct Answer: B

When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.

If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write

a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.

Topic 6, Main Questions Set C

Question No. 3

Your company currently runs a large on-premises cluster using Spark Hive and Hadoop Distributed File System (HDFS) in a colocation facility. The duster is designed to support peak usage on the system, however, many jobs are batch n nature, and usage of the cluster fluctuates quite dramatically.

Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more servers offerings m order to take advantage of the cloud Because of the tuning of their contract renewal with the colocation facility they have only 2 months for their initial migration How should you recommend they approach thee upcoming migration strategy so they can maximize their cost savings in the cloud will still executing the migration in time?

AMigrate the workloads to Dataproc plus HOPS, modernize later

BMigrate the workloads to Dataproc plus Cloud Storage modernize later

CMigrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery

DModernize the Spark workload for Dataflow and the Hive workload for BigQuery

Show Answer

Correct Answer: D

Question No. 4

You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat

a. What should you do?

Choose 2 answers

AConfigure your Dataflow pipeline to use local execution.

BModify your Dataflow pipeline lo use the Flatten transform before writing to Bigtable.

CModify your Dataflow pipeline to use the CoGrcupByKey transform before writing to Bigtable.

DIncrease the maximum number of Dataflow workers by setting maxNumWorkers in PipelineOptions.

EIncrease the number of nodes in the Bigtable cluster.

Show Answer

Correct Answer: D, E

https://cloud.google.com/bigtable/docs/performance#performance-write-throughput

https://cloud.google.com/dataflow/docs/reference/pipeline-options

Question No. 5

Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

A1Create a project for storage of the data for your organization.
2 Create a central Cloud Storage bucket with three folders to store the files for each department.
3. Create a central BigQuery dataset with tables prefixed with the department name.
4 Give viewer rights for the storage project for the users of your departments.

B1Create a project for storage of the data for each of your departments.
2 Enable each department to create Cloud Storage buckets and BigQuery datasets.
3. Create user groups for authorized readers for each bucket and dataset.
4 Enable the IT team to administer the user groups to add or remove users as the departments' request.

C1 Create multiple projects for storage of the data for each of your departments' applications.
2 Enable each department to create Cloud Storage buckets and BigQuery datasets.
3. Publish the data that each department shared in Analytics Hub.
4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.

D1 Create multiple projects for storage of the data for each of your departments' applications.
2 Enable each department to create Cloud Storage buckets and BigQuery datasets.
3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones.
4 Enable each department to own and share the data of their data lakes.

Show Answer

Correct Answer: C

Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.

Architecture and functions in a data mesh - Google Cloud

Professional Data Engineer Certification Exam Guide | Learn - Google Cloud

Build a Data Mesh with Dataplex | Google Cloud Skills Boost

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