Six 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.
These exams are designed and developed by the author of the official Google Cloud Professional Data Engineer exam guide and a data architect with over 20 years of experience in databases, data architecture, and machine learning. These exams will test how well you understand how to ingest data, creating data processing pipelines, work with both relational and NoSQL databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc and more. You will also be tested on how to map business requirements to technical solutions, especially around problems in compliance and security.
Who this course is for:
- Anyone who wants assess the Google Professional Data Engineer exam before appearing for the actual exam
- Anyone interested in becoming a data engineer or data analyst