Professional Data Engineer Certification
Professional data engineer certification enables data-driven decision-making by collecting, transforming, and publishing data. By data collecting, transforming, and its publishing he who enables the decision making of data-driven is known as the professional data engineer. Professional data engineers should have the ability of data processing system’s designing, building and make it work and monitoring with the help of security and compliance emphasis. And with the help of the particular emphasis on portability & flexibility, efficiency & scalability, etc.
How Can You Become Professional Data Engineer?
There are five simple steps to follow if you want to be a Professional Data Engineer by achieving this exam certification, let’s enlist them:
- Finish all the tracks on Coursera Data Engineer Courses.
- Take the Professional Data Engineer course on one of the trustworthy websites.
- Read the best practice guide online.
- Go through the exam description page properly.
- Download the Professional data engineer Chart Sheet for your work.
- Make sure to be clear about the exam(topics and other details).
- Let’s start your personal practice, via Sample tests, online guides, and courses.
Exam Topics Of Professional Data Engineer Certification
The learning models of the pre existing machine’s continuous training and deployment should also be known to a professional data engineer, he should also be familiar with it. There are several sections explaining the Professional Data Engineer Exam and you have to study these four domains properly before applying for the exam certification.
- Ensuring Solution Quality
- Data process system’s building and working.
- Data processing system’s designing
- Making the machine learning models work.
Data Processing system’s Designing: Storage technologies that are suitable and best are selected. In this the considerations are modeling of data, system’s distributions, design of Schema and to meet the requirements of business the storage systems are also mapped. Pipelines of data design include the publishing, batch, visualization and streaming of the data. It also includes the predictions between the batch and online & orchestration and automation of the Job are also the essential components under consideration.
The solutions of the data processing are designed which includes the infrastructure of your choice, planning of full capacity and computing of the edge. It includes the cloud which is hybrid and options for architecture in which queues of messages and brokers of the messages etc are involved. Availability of the system, tolerance for the faults up there and the usage of the systems that are distributed are considered. Processing and warehousing of data migration is also the part of this section which includes the migration’s validation and the way of the migration of design to a future state. It also includes the awareness for the present state.
Data Process system’s building and working: Building of storage system and its working includes the considerations like effective usage of services those are managed performance and the costs of storage, and the data’s lifecycle management. Secondly, the pipelines building and working which includes the cleansing, streaming, and batching of data. Import of data and its acquisition and with the new data sources the integration is also included under the consideration. Last step in this section is the processing infrastructure’s building and it’s working in which the considerations are the resources those are provision, pipeline’s monitoring and adjustment and the control of testing and quality.
Making the machine learning models to work: in this section as a service the leveraging of Machine learning models that are pre-built. It includes considerations like APIs of machine learning, machine learning API’s customization and the experiences that are conversational.
Pipelines of machine learning deployment includes the machine learning model’s retraining, an evaluation that is continuous, and the ingestion of data that is appropriate. Training that is suitable is chosen and the infrastructure is served which includes the machine is single or machine is distributed, compute that is edge it’s usage and the accelerators of hardware i.e. TPU and GPU
Machine learning models measurement, troubleshooting, and monitoring consider the terminologies of machine learning, machine learning model’s dependencies impact and the source of error that is common.
Quality of solution Ensurance: security designing and compliances design is the first step of this section which includes the considerations like management of access and the management of identities. Security of data, privacy ensurance, and compliances that are legal are also included.
The second step is the efficiency and scalability ensurance. It includes test suites building and it’s working and monitoring of the pipelines. The representation of data and processing infrastructure of data improved, accessed and there issues are resolved also included in the consideration. Resources are resized and autoscaled.
The fidelity and reliability also ensured in this section and the consideration are the control of quality and the preparation of data performances, monitoring and verifications. For the data recovery the planning, executions and the deep testing took place and the choice between the requirements like ACID, idempotents etc.
The last step of this section is the flexibility and portability ensurance and it include the following like mapping to the requirements of current and future businesses, for the portability of data and application designing took place and at last discovery, cataloging, and staging of data took place.
Summary of Exam Certification:
The exam for the certification of the professional cloud architect is of about 2 hours ( 120 minutes ). The registration fee is 200 US Dollars plus the taxes are also there or where it is applicable. It consists of multiple-choice questions and multiple-choice questions taken remotely or in person at a test centre, to be taken from the candidate in the paper centre. Exam is taken from the onsite proctored remote location or the testing centre and the languages under which the exam is executed are English and Japanese. There are no prerequisites for this exam certification, and recommended experience for this certification is almost 3+ years including industry experience, 1+ years designing and managing solutions using Google Cloud.
Getting this certification is not so easy, but it’s not very tough either, your efforts and hard work can ExamOut.co help you to qualify for certification, and also you should have basic knowledge and experience of the skills of professional data Engineer certification.