Google Professional Data Engineer
- Exam: Professional Data Engineer (Professional Data Engineer on Google Cloud Platform)
- Certification: Professional Data Engineer
- Certification Provider: Google
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Professional Data Engineers are a huge boon to companies of all kinds. These people can effectively collect, transform, and publish data so as to enable decision making based on the given results. The Google Professional Data Engineer certification verifies oneâ€™s abilities in this field, including skills in designing, securing, and maintaining data processing systems. The curriculum places special emphasis on the safety, scalability, reliability, flexibility, and efficiency of these systems and ensures the candidate can properly utilize, implement, and consistently train machine learning models.
There are valuable skills and knowledge one should master to ace the corresponding test and get accredited by Google. All the information concerning certification is provided below.
Who Is This Certificate for?
Google Professional Data Engineer requires a deep and critical understanding of its subject matter and thus will place certified individuals higher in the eyes of potential employers. Considering the benefits one will gain, the existence of some prerequisites is obvious. Although these are only recommendations, candidates are expected to have 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud Platform. However, anyone can attempt the test if they are ready to study all the exam domains.
Individuals will have 2 hours to answer several multiple choice and multiple select questions. The exam is available in both English and Japanese languages and has a fee of $200. When it comes to the format, one can choose to sit for an online-proctored exam from a remote location or take it on-site at one of the approved testing centers.
From big data to machine learning, the Professional Data Engineer exam encompasses an array of topics that will help you achieve peak performance in the field. Detailed below are the most prominent domains and relevant subdomains presented in the test.
1. Planning and designing data processing systems
This unit covers the initial stages of building a cloud-based data processing system. The data engineerâ€™s awareness of the available technologies, the ability to cater to the scale, storage requirements, and interactivity of businesses, and skills in building flexible designs for frictionless migration and expansion of services are the key components that are assessed here. The 4 sub-domains include:
- Incorporation of suitable storage technologies
The main focus of this subsection is a variety of storage technologies and how to choose the right one while taking into account the factors like storage systemsâ€™ mapping, tradeoffs related to latency, schema design, and the distribution of systems with the data model.
- Effective Data pipeline design
This subdomain will assess the candidate's skills in designing data pipelines and workflows using Google Cloud products like BigQuery, providing data streaming and batching, and utilizing the services like Cloud Composer for orchestration.
- Analyzing and deciding on suitable data processing solutions
Understanding of choosing the infrastructure, how to define expected capacity, and system availability when coming up with processing solutions will be tested through this topic. Candidates will need to be familiar with selecting appropriate servers, software, services, etc., should know how to establish required levels of tolerance and backup plans for interruptions, and using technologies such as edge computing and Hybrid Cloud.
- Migration of data and processes
Being able to come up with a design that fully supports and supplements the migration from the current state to a future state, one will surely succeed in this subdomain. The validation of migration as well as using common tools and services are the topics covered here as well.
2. Developing and embedding operations into data processing systems
This domain is important as it is dedicated to oneâ€™s knowledge and hands-on skills in providing solutions to businesses. You are required to be able to integrate a variety of services and ensure an efficient and sustainable solution based on the needs of the company. This section includes the following sub-topics:
- Implementing storage systems
Exam-takers will have to be fluent in using Google Cloud services such as Spanner, Bigtable, SQL, Storage, Datastore, and Memorystore. Furthermore, they need to be aware of how to find the right balance between performance and storage costs and manage data across all stages of its lifecycle.
- Implementing pipelines
Individuals need to have sound knowledge on how to execute data processing operations such as acquiring, transformation, data cleansing, streaming, and batching.
- Implementation of infrastructure
The main objective of this subdomain is measuring the applicant's competence in managing software and hardware to establish smooth and consistent performance. Here, understanding the techniques for monitoring, modifying, and testing pipelines is essential.
3. Utilization of machine learning models
This part of the exam content verifies the candidate's knowledge of the different Google Cloud ML products available to solve various real-life problems. A successful candidate should be able to leverage existing machine learning models to analyze large sets of data and make data-driven decisions. Below are the 4 main sub-domains:
- Utilizing pre-built machine learning models
Exam-takers are expected to have a thorough knowledge of the diverse APIs used for analyzing various types of data including text, images, and speech. The main models that they need to be familiar with are Vision API, Speech API, Dialogflow, etc.
- Successfully implementing ML pipelines
The ability to channel appropriate Big data for processing and analysis and ensuring continuous evaluation are some of the key aspects that this subsection focuses on. Hands-on experience in working with products such as Cloud Machine Learning Engine, BigQuery ML, Kubeflow, and Spark ML is assessed.
- Choice of infrastructure
Possession of the skills needed to make appropriate choices on hardware accelerators, technologies, and systems based on computing requirements, location, network speeds, storage volumes, etc. will be expected from the applicant.
- Measuring, monitoring, and fixing errors in machine learning models
The exam-taker needs to be familiar with the machine learning terminology and should have a deep understanding of the back end of machine learning models to successfully identify errors and resolve them efficiently.
4. Delivering a quality solution
The following are the major aspects covered by this domain:
- Ensuring secure and compliant designs
The candidate has to be aware of the Acts and regulations that govern the use of sensitive and/or valuable data such as medical, financial, etc. and should be able to design and implement a solution that is compliant with the existing standards and abide by good security practices.
- Ensuring scalability and effectiveness
This involves the individuals knowing how to develop test suites and deploy them to ensure efficient functionality. They will also need to understand the various aspects of resizing and autoscaling resources within a particular design.
- Ensuring a system achieves results and is reliable
This requires the candidate to be confident in using tools like Cloud Dataprep for quality control and performing stress testing to determine fault tolerances and other system-related statistics.
- Ensuring the solutionâ€™s versatility
Exam-takers will need to be competent in making sure their solutions meet current and future business requirements such as multi-cloud, data residency, data staging, and more.
Google Cloud certifications are valid for 2 years from the date of completion. individuals will need to retake the exam to renew their accreditation. Note that there is a 50% discount on your recertification test as one of the benefits of being a part of the Google Cloud community.
Job Prospects and Certification Path
Data engineering is rapidly becoming a valuable skill in the global market. As technology develops, the amount of data created as a whole grows exponentially, requiring the aid of qualified professionals to streamline it. As such, the demand for such employees is only set to increase, worldwide, along with their salaries that are of about $92k on average, according to PayScale, with an opportunity to reach a mark of $132k annually.
Moreover, one can broaden the area of cloud expertise by earning other certificates offered by Google. These include Cloud Security Engineer, Machine Learning Engineer, Cloud Architect, and many others.
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- Incorporation of suitable storage technologies
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