Google Cloud Big Data & Machine Learning Fundamentals course
This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:
- Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
- Hire BigQuery and Cloud SQL for interactive data analysis
- Create ML models using BigQuery ML, APIs, and AutoML.
- Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
Certification Path
The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.
You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
Reference: https://cloud.google.com/certification/data-engineer
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Introduction
Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.
The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.
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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Maintaining and automating data workloads (~15% of the exam) | 15% | - Monitoring data pipelines and data processes
|
| Topic 2: Designing data processing systems (~30% of the exam) | 30% | - Designing data pipelines
|
| Topic 3: Storing the data (~20% of the exam) | 20% | - Selecting storage systems
|
| Topic 4: Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
| Topic 5: Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
|





