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To prepare for the Google Professional-Data-Engineer exam, candidates can take advantage of the many resources available online. Google offers a variety of training courses and study materials, including online courses, practice exams, and study guides. Additionally, there are many study groups and forums where candidates can connect with other professionals who are preparing for the exam. With the right preparation and dedication, professionals can achieve certification as a Google Certified Professional Data Engineer and demonstrate their expertise in Google Cloud Platform.

The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

• Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
• Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
• Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
• Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;

Reference: https://cloud.google.com/certification/data-engineer

The Google Professional-Data-Engineer exam tests the candidate's proficiency in working with various data processing tools, including Google Cloud Platform technologies such as BigQuery, Dataflow, and Cloud Storage. Professional-Data-Engineer exam also covers topics such as data modeling, data ingestion, data transformation, and data analysis. Additionally, the exam tests the candidate's understanding of best practices for data engineering, including performance optimization, security, and compliance.

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The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing systems, data analysis, machine learning, and data security on Google Cloud Platform. Candidates are expected to have a thorough understanding of these topics and be able to apply them in real-world scenarios.