• views: 48
• date: 17 August 2026
• posted by: voska89

What you'll learn

⚡ Match a business requirement to the correct GCP storage service: Cloud SQL, BigQuery, Bigtable, Spanner, or Firestore
⚡ Design streaming pipelines with Pub/Sub and Dataflow including windowing, watermarks, and exactly-once semantics
⚡ Choose between Dataflow, Dataproc, Data Fusion, and Dataform for a given transformation scenario
⚡ Design BigQuery partitioning, clustering, and materialized view strategies to minimize cost and latency
⚡ Design Bigtable row keys that avoid hotspotting for high-throughput workloads

Requirements

❗ Strong working SQL proficiency and familiarity with ETL/ELT, data modeling, and pipelines
❗ Helpful: 1+ year building data solutions on Google Cloud, distributed-systems basics, and Associate Cloud Engineer or equivalent hands-on GCP experience

Description

This course contains the use of artificial intelligence.
This course delivers exam-focused preparation for the Google Cloud Professional Data Engineer certification, built around the exact five domains in Google's current exam guide and the judgment-based, scenario-heavy question style candidates actually encounter. Instructor Aseem Mankotia structures every chapter around a single exam objective — matching a described business requirement to the correct GCP service and design pattern, whether that's BigQuery vs Bigtable vs Spanner, Dataflow vs Dataproc, or BigQuery ML vs Vertex AI — rather than reciting product definitions you can already find in documentation.
Each chapter follows a strict no-fluff format: the exam domain and its approximate weight, the concepts most heavily tested, a hands-on design walkthrough using exact current GCP service names, the classic traps that catch candidates off guard (partitioning vs clustering, exactly-once vs at-least-once, Bigtable hotspotting, IAM vs VPC Service Controls), and scenario-style practice questions modeled on real exam phrasing. Labs are read-and-understand design exercises — architecting a Pub/Sub plus Dataflow streaming pipeline, designing a Bigtable row key, wiring a Cloud Composer DAG, configuring BigQuery column-level security — because the exam tests design judgment, not console clicks.
The course closes with a full 120-minute, ~55-question exam simulation across all five domains plus a pacing and triage strategy for the real test. This is designed as a focused companion to the Professional Machine Learning Engineer and Associate Cloud Engineer courses in this catalog for anyone building a Google Cloud data career. GCP service names and pricing evolve, so always cross-check details against current Google Cloud documentation before your exam date; exam fee (~USD 200, varies by region) and the two-year certification validity should also be confirmed on Google's site.
AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official Professional Data Engineer exam guide.

Who this course is for

⭐ Data engineers, ETL/pipeline developers, and analytics/ML-platform practitioners on Google Cloud seeking exam-focused Professional Data Engineer preparation

Homepage

https://www.udemy.com/course/google-professional-data-engineer-exam-prep

   
   
   

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