Get confidence for pass the Google Cloud Professional Data Engineer Exam
Learn How to secure app & service inside GCP environment required for this certification
Practice taking scenario-based questions about data engineering
Practice taking scenario-based questions about data engineering
Three practice exams having scenarios related to Data Engineering aspect of GCP which enable you to master Google Professional Data Engineer Certification Exam
- Assess yourself for the Google Professional Data Engineer Certification Exam
- Ensure that you are fully prepared for the exam
- Appear for these exams only when you feel you are ready to take the exam
Topics covered in the exams –
- Storage – BigQuery, BigTable, Spanner, Cloud DataStore, Cloud Storage etc.
- Processing – DataProc, DataFlow, Spark, Beam etc.
- Analysis – BigQuery, Hive etc.
- Visualization – DataStudio
- Ingestion – Cloud Pub/Sub, Kafka etc.
- Modeling – ML APIs, ML Concepts, AI Platform, Accelerator, Troubleshooting etc.
- Misc – Dataprep, Data Catalog, Auto Scaling, Stackdriver, IAM etc
Note:
This course is not dump of the actual exam but it is for assessing your preparation before the real exam.
Below are our more courses –
- Big Data Crash Course | Learn Hadoop, Spark, NiFi and Kafka
- Big Data For Architects | Build Big Data Pipelines and Compare Key Big Data Technologies
- Google Data Engineer Certification Practice Exams
- Setup Single Node Cloudera Cluster on Google Cloud
What can a data engineer certification do for you? The need for data engineers is constantly growing and certified data engineers are some of the top paid certified professionals. Data engineers have a wide range of skills including the ability to design systems to ingest large volumes of data, store data cost-effectively, and efficiently process and analyze data with tools ranging from reporting and visualization to machine learning. Earning a Google Cloud Professional Data Engineer certification demonstrates you have the knowledge and skills to build, tune, and monitor high-performance data engineering systems.