Practice taking scenario-based questions about data engineering
Identify appropriate data engineering technology solutions
Apply principles of machine learning training and evaluation
Operationalize machine learning models
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
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.