Realistic BigQuery, Dataflow & pipeline scenario questions with explanations to pass the Google Data Engineer exam
Sub Category
- IT Certifications
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Objectives
- Pass the Google Professional Data Engineer (PDE) exam on your first attempt
- Master all five PDE domains weighted like the real exam blueprint
- Design scalable data processing systems and choose the right GCP services
- Build batch and streaming pipelines with Dataflow, Pub/Sub, and Dataproc
- Master BigQuery optimization — partitioning, clustering, and cost control
- Select correctly among BigQuery, Bigtable, Spanner, and Firestore
- Orchestrate workloads with Cloud Composer (Airflow)
- Prepare data and features for ML with Vertex AI and BigQuery ML
- Secure and govern data with IAM, DLP, and Dataplex
- Reason through real-world data engineering trade-offs with confidence
Pre Requisites
- A general understanding of data engineering concepts (ETL, pipelines, SQL)
- Hands-on experience with Google Cloud data services is strongly recommended
- Around 3+ years of industry experience, including 1+ year on GCP, is ideal
- Familiarity with SQL and a programming language such as Python
- Some exposure to machine learning concepts is helpful
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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