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About Google Professional Data Engineer Exam

Google offers the whole certification program for those candidates who want to know more about Cloud, and the Professional Data Engineer exam is one of the options in this professional-level track. This qualifying test grants you the Google Cloud certificate and proves that you can enable the data-driven decision making by collecting, transforming, as well as publishing data. Those individuals who hold the Google Professional Data Engineer certification have the skills in designing, building, operationalizing, securing, and monitoring data processing systems with a particular emphasis on compliance and security.

To be eligible for this Google certification, you don’t need to obtain any other certificates or pass any prior tests, but it is recommended that you have some experience in the industry and work at least 3 years in it. If you know how to design and manage solutions using GCP and you have been doing it for a year or more, this level of expertise will also help you understand the concepts covered in the prerequisite exam.

Exam Details

Before going for the certification test, you need to know what to expect from it. Thus, you should be ready for the multiple-choice as well as multiple-select questions, which you must clear within 120 minutes. The exam is available in English or Japanese and you will need to choose one of the options during the registration process. Also, you will have to choose the delivery method of the test (there are two options). You can go for the onsite proctored version and sit for the exam at any testing center or opt for the online exam to take it from a remote location. To register for this test, you should pay $200.

To prepare for this qualifying test, you can take the Big Data & Machine Learning Fundamentals course and enroll for Data Engineering on Google Cloud Platform. These training options can help you get started with big data & machine learning as well as know about the design of data processing systems, building of the end-to-end data pipelines, and learn how to analyze data. There are also two more courses that you can take, and one of them expands your understanding of Dataflow and teaches you the essential skills that you can apply to your first Google Cloud project. Other training course prepares you exactly for the certification exam.

Exam Topics

To be able to answer all the questions during the test, you need to know which topics to learn beforehand. Therefore, it is important to explore the details in the exam blueprint and check the information on the official site to know if there were any changes in the content. All in all, the test contains the following domains:

  • Designing data processing systems

    This objective covers the details of the selection of the appropriate storage technologies, design of data pipelines and data processing solutions, as well as migration of data processing and data warehousing. Therefore, you should know about the processes, such as data publishing & visualization, job automation & orchestration, capacity planning, as well as system availability & fault tolerance. You should also have knowledge of the awareness of the current state and the ways of how to migrate a design to a future state. Besides that, it is important to have the skills in mapping storage systems to the business requirements, validating a migration, and using the distributed systems. Your knowledge base should also include the information about schema design, online vs. batch predictions, as well as choice of infrastructure.

  • Building & operationalizing data processing systems

    As for this topic, the potential candidates should know how to build and operationalize storage systems, processing infrastructure, and pipelines. This means that they should know about data cleansing, data acquisition & import, batch & streaming, storage costs & performance, as well as lifecycle management of data. Moreover, you are required to have the skills in adjusting pipelines and monitoring them, provisioning resources, and know about testing & quality control.

  • Operationalizing machine learning models

    In this domain, the individuals’ skills in deploying an ML pipeline, leveraging the pre-built ML models as a service, and choosing the appropriate training & serving infrastructure will be evaluated. These subareas include the details of the conversational experiences, ML APIs, continuous evaluation, hardware accelerators, and ML APIs customization. You should also have the ability to use edge compute, ingest appropriate data, and retrain the machine learning models. Besides that, the examinees need to have knowledge of how to measure, monitor, and troubleshoot the machine learning models. Therefore, it is important to know the machine learning terminology, learn about the common sources of error, and understand the impact of the dependencies of the machine learning models.

  • Ensuring solution quality

    The last section is all about the design for security and compliance. You are required to know how to ensure scalability and efficiency, flexibility and portability, as well as reliability and fidelity. Thus, it is important to know about identity and access management, data security, pipeline monitoring, verification and monitoring, as well as data cataloging, staging, and discovery. Furthermore, your skillset should include the abilities to choose between ACID, idempotent, eventually consistent requirements, perform data preparation & quality control, and build and run test suites. If you know how to map to the current and future business requirements as well as design for data & app portability, it will be a huge advantage during the test.

Career Advantages

After passing the test with a great result and obtaining this Google certification, you will find out that there are many new doors have opened for you. This means that you will be able to land a more prestigious job and ask for a raise. Thus, you can become a Data Analyst, a Google Cloud Engineer, a Software Engineer, a Sr. Systems Engineer, a Google Cloud Data Modernization Engagement Lead, a Customer Engineer, an Ansible Automation Engineer, or an IT Engineer. As for the salary that you can expect, your average income can be about $137,776 per year.

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