The Google Cloud Professional Data Engineer Exam is designed for professionals who are responsible for designing, building, managing, and analyzing data processing systems on Google Cloud. It’s ideal for data engineers, data analysts, and cloud professionals who want to validate their ability to work with large-scale data systems on Google Cloud Platform (GCP). This certification is suited for individuals with experience in data processing, storage, analysis, and machine learning, and those looking to demonstrate their skills in handling complex data environments within the cloud.

Certification Name: Google Cloud Professional Data Engineer
Administered By: Google
Exam Format: Online Proctored exam

• The exam consists of multiple-choice and scenario-based questions.
• You’ll be presented with real-world scenarios that test your ability to design, build, manage, and analyze data processing systems on Google Cloud.
  Duration: 2 Hours (120 Min)
  Languages: Primarily English
  Prerequisites: None specified, but a strong understanding of data engineering concepts, Google Cloud Platform (GCP), and experience with data processing and analysis tools is beneficial.

Exam Objectives:

1. Designing Data Processing Systems:

   • Understanding how to design and build scalable, secure, and efficient data processing systems using GCP tools, such as BigQuery, Dataflow, and Dataproc.
   • Implementing batch and streaming data pipelines.

2. Building and Operationalizing Data Pipelines:

   • Developing and deploying data pipelines to process and analyze data effectively.
   • Using Cloud Data Fusion, Cloud Composer, and other tools for orchestrating and automating workflows.

3. Analyzing and Visualizing Data:

   • Working with data storage, transformation, and analysis tools to process and visualize data, including BigQuery, Data Studio, and Looker.
   • Designing and implementing data models for analysis and reporting.

4. Data Security and Governance:

   • Implementing data security, privacy, and governance policies within Google Cloud environments.
   • Using tools like Cloud IAM, Cloud KMS, and Data Loss Prevention API to manage access and ensure compliance.

5. Machine Learning and Advanced Analytics:

   • Leveraging machine learning and AI services, such as BigQuery ML, to perform advanced analytics on structured and unstructured data.
   • Building and deploying machine learning models within the Google Cloud environment.

6. Monitoring, Optimizing, and Troubleshooting:

   • Using Google Cloud’s operations suite (formerly Stackdriver) for monitoring and troubleshooting data processing systems.
   • Optimizing the performance and cost-effectiveness of data pipelines and processing systems.

7. Managing Data Infrastructure and Resources:

   • Understanding the architecture and resource management of GCP’s data solutions.
   • Managing costs and resource utilization effectively.

8. Collaboration and Data-driven Decision Making:

   • Supporting business intelligence and data-driven decision-making by providing accessible and actionable data insights to stakeholders.

Validity:

• The Google Cloud Professional Data Engineer certification is valid for two years. To maintain your certification, you’ll need to recertify by passing the current exam or a relevant updated exam before the certification expiration date.

Preparation Tips:

• Gain hands-on experience with GCP data services, such as BigQuery, Dataflow, Dataproc, and Cloud Storage.
• Study the official exam guide, Google Cloud’s documentation, and training materials.
• Consider completing the Google Cloud Professional Data Engineer learning path, which includes practice exams and training resources.

Good luck on your journey to becoming a Google Cloud Professional Data Engineer!