How to Prepare for Google Professional Data Engineer Certification Exam

Certification Overview:

The Google Cloud Certified Professional Data Engineer is an advanced certification focusses on services related to storage, data processing, machine learning and artificial intelligence. A highly valued certification in the industry which certifies an individual to make data-driven decision-making by collecting, transforming, and publishing data. Proofs the ability of a data engineer to design and implement data engineering solutions in Google cloud with a particular emphasis on security, compliance, scalability, efficiency, reliability, fidelity, flexibility, and portability. Google Cloud Certified Professional Data Engineer will be a highly skilled individual who excels in cloud-based data engineering solutions.

Ideal candidate to take this exam:

It’s an advanced professional certification exam which expects basic associate level of understanding in Google Cloud Platform. The main thing you need to know about this certification exam is that it isn’t a theoretical test or can crack it with a bunch of question dumps. It is designed in such a way to confirm the skills of cloud practitioner who can solve the data engineering problems.

Learning curve from this exam:

Design data processing systems:

  1. Selecting the appropriate storage technologies by mapping storage systems to business requirements, using data modelling, latency involved trade-offs, attaining throughput, consistent transactions, and effective schema design.
  1. Designing data pipelines which includes data publishing , data visualization, batch data processing, streaming data processing, interactive vs batch data predictions, job automation and orchestration. Google services and other technologies covered in this section: BigQuery, Cloud Composer, Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub and Apache Kafka.
  1. Designing a data processing solution with the choice of infrastructure system availability, fault tolerance, use of distributed systems, capacity planning, hybrid cloud design and edge computing. Enables you with several architecture options like message brokers, message queues, middleware, service-oriented architecture, serverless functions and at-least once, in-order, and exactly once, etc., event processing.
  1. Migrating data warehousing and data processing by understanding the awareness of current state and how to migrate a design to a future state, migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking) and validating a migration.

Build and operationalize data processing systems:

1.     Building and operationalizing storage systems with effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore), optimized storage costs and performance and life cycle management of data.

2.     Building and operationalizing pipelines by cleansing the data, data processing in batch and streaming mode, data transformation, data acquisition, import from external sources and integrating with new data sources.

3.   Building and operationalizing processing infrastructure by provisioning resources, monitoring pipelines, adjusting pipelines, testing process flow and quality control.

Operationalize machine learning models:

  1. Leveraging pre-built ML models as a service which includes ML APIs, customizing ML APIs and conversational experiences. Google services covered in this section: Vision API, Speech API, AutoML Vision, Auto ML text and Dialogflow.
  1. Deploying an ML pipeline by ingesting appropriate data, retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML) and continuous evaluation.
  1. Choosing the appropriate training and serving infrastructure by selecting between distributed vs. single machine, use of edge compute and use of hardware accelerators (e.g., GPU, TPU).
  1. Measuring, monitoring, and troubleshooting machine learning models by understanding the machine learning terminologies (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics), impact of dependencies of machine learning models and common sources of error (e.g., assumptions about data).

Ensure solution quality:

  1. Designing for security and compliance by understanding Identity and access management (e.g., Cloud IAM), learning data security (encryption, key management), ensuring privacy (e.g., Data Loss Prevention API) and designing a solution which supports all legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR)).
  1. Ensuring scalability and efficiency by building and running test suites, pipeline monitoring (e.g., Cloud Monitoring), assessing, troubleshooting, improving data representations, data processing infrastructure, resizing and autoscaling resources.
  1. Ensuring reliability and fidelity by performing data preparation and quality control (e.g., Dataprep), verification, monitoring, planning, executing, stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis) and choosing between ACID, idempotent, eventually consistent requirements.
  1. Ensuring flexibility and portability by mapping to current and future business requirements, designing and application portability (e.g., multi-cloud, data residency requirements), data staging, cataloging and discovery.

Preparation for the certification exam:

The Professional Data Engineer certification is not a theoretical test that we can crack with some tutorials. It requires immense knowledge about the services offered in Google Cloud and associate level knowledge in data engineering solutions. The candidate should have profound hands-on experience with Google Cloud Platform and data processing related technologies. This certification has been designed in a way that confirms the skills required of a practitioner which tests whether the candidate know how to do the job of a data engineer.

There are two approaches to start the preparation:

  1. Top Down
  2. Bottom Up
  • Top-Down method works for a candidate who must start from the scratch and master all the required skills of a data engineer. This method starts with the basic information that the candidate has already hold the knowledge in data engineering domain, review that information and make sure the candidate has mastered and recall most of it. Then do the research and practice until they become proficient and then attend the exam. This approach focuses much more quickly on what you need to learn and less time spent reviewing information you already know.
  • Bottom-up method identifies the key points and categorizes them into complex or subtle. In this way, it’s easier to define an indicator for the elements that the candidate has knowledge and the elements that they are missing or weak on some aspects. The candidate can note down these indicators and use that as a guide for what to study. We can fill in the gaps on what you need to know by going back to the respective training content) or by exploring documentation or labs to solidify your understanding; keep practicing the problem-solving skills as a data engineer which makes you proficient, then you can attempt the exam. This approach helps not to skip any topic and on the other hand, doesn’t let us spend more time on known concepts/topics.

Getting ready for Professional Data Engineer certification exam needs reference of multiple course material. The online courses and tutorials make you understand the Google services using in data engineering domain theoretically. The on-hand practice with the theoretical knowledge enables you to pass the certification and master the designing and implementation of data engineering solutions. The courses and online platforms for learning and practicing for the certification are:

1. Professional Data Engineer Certification Course by Google:

This online video tutorial presented by Google explains the topics that you should be aware which are covered in certification exam curriculum. The presenter clearly explains the items which are part of the certification curriculum and provides you tips in each item to be considered as important for cracking the exam. This course comprises of 120 mins of videos tutorials, 240 mins of fee lab sessions with Qwiklabs, reference documents, case studies and practice exam questions. You can spend 2 days for clear total glimpse of this course and understand all the topics needed to be mastered to crack the certification exam.

Course Link:

2. Google Cloud Certified Professional Data Engineer Study Guide by O’Reilly:

This is an e-book written by Dan Sullivan; provides the detailed explanation on what to prepare for this exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests.

E-book Link:

3. Google Cloud Professional Data Engineer: Get Certified 2022 by Udemy:

This online video tutorial is a high-level glimpse about the action items that prepares you well for the certification exam. This is a 6.5 hours of video presentation with topics included:

  • How to pass the Google Cloud Professional Data Engineer Exam.
  • Build scalable, reliable data pipelines.
  • Choose appropriate storage systems, including relational, NoSQL and analytical databases.
  • Apply multiple types of machine learning techniques to different use cases.
  • Deploy machine learning models in production.
  • Monitor data pipelines and machine learning models.
  • Design scalable, resilient distributed data intensive applications.
  • Migrate data warehouse from on-premise to Google Cloud.
  • Evaluate and improve the quality of machine learning models.
  • Grasp fundamental concepts in machine learning, such as backpropagation, feature engineering, overfitting and underfitting.

Course Link:

4. GCP – Google Cloud Professional Data Engineer Certification by Udemy:

This is a 24 hours of online video tutorial presented by Ankit Mistry; starts with basic grasp of data engineering and database concepts and leads deep towards into the cloud services offered by Google for data engineering solutions. This course has 12 articles, 6 downloadable resources, full lifetime access after enrollment, access on mobile and web, closed captions for better understanding and provides you the certification of completion.

Course Link:

5. Practice Questions by Examtopics:

Examtopics is an important online question bank that prepares you for the Professional Data Engineer certification exam. This online portal helps you with the question patterns by answering many practices tests. There is no guarantee that the questions given here will be there in the exam as well. Its only to prepare for the question patterns and real-world scenarios/case studies being asked in the certification exam. Every practice question has an explanation part which tells you about the correct and incorrect answers and justification behind correct answer. It also has the comments section for each question, where other GCP practitioner discuss about the correct answer which helps you to understand the problem and solution better.

Practice Questions Link:

6. Google Cloud Trial Account:

Google offers trial accounts for practitioners with $300 in free credits. This trial account enables you to do the on-hand practice for all the Google cloud services you learned theoretically. You can explore multiple options available in each cloud product and learn new things which may not be available in online learning materials. Utilize the free credits wisely to accommodate hands-on experience with all the services; ensure that you delete/clear all the cloud resources when they are not in use, to avoid unnecessary utilization of free credit.

Reference Link:

Preparation Tips:

  • Prepare a roadmap with all the necessary learning and practicing materials. This includes all online video tutorials, e-books, practice questions and time for hands-on practice with Google trial account.
  • Set a deadline to take up the certification considering above roadmap and your availability. You need to consider only the time you can fully focus on the learning path.
  • Create a consistent timeline every day till the day of exam. Since the Professional Data Engineer certification has a wide variety of topics to be covered, consistently you need to spend couple of hours daily for your preparation. Leaving time gaps in your learning path may lead to spend lot of hours of revising the items that you have already learnt.
  • Utilize the free lab sessions in the online courses to get proper hands-on experience after finishing each topic. This practice enables you to explore all the features and options available in each service which are not shown in the learning materials.
  • Google trial account is a great deal to get more exposure on the cloud products. Just avoid unnecessary resource utilization and lose the free credit.
  • Keep a bullet point notes to review the important details from each topic; the certification wide variety of topics from Google cloud services and data engineering and you must remember all the key things in each topic.
  • Keep working on the practice exam questions and case studies to get familiarized with the question patterns.
  • On the exam day, read and understand the question properly before answering, bookmark the question which you are unsure and revisit at last before submission.

All the best for your certification journey!

About the author


Youssef is a Senior Cloud Consultant & Founder of

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