Google Certified Professional Data Engineer: First Edition - IPSpecialist

Google Certified Professional Data Engineer: First Edition

Course Information

Google Certified:

Professional Cloud Data Engineer: First Edition – 2022

  • Covers Complete & Official Exam Blueprint
  • Summarized content
  • Case study based approach
  • Ready to practice labs
  • Exam tips
  • Mind maps
  • 100% passing guarantee

791 Students Enrolled


US$23.99 $29.99

About Google Certified Professional Data Engineer Exam


Exam Questions Case study, short answer, repeated answer, MCQs
Number of Questions 80-100
Time to Complete 120 minutes
Exam Fee 200 USD


The Google Professional Data Engineer Exam certifies your skills and knowledge in the areas where you want to work. If a candidate wishes to work as a Google Professional Data Engineer and demonstrate his competence, Google offers certification. This Google Professional Data Engineer Certification verifies a candidate’s knowledge of Big Data and Data Engineering.

The Professional Data Engineer exam assesses your ability to:

  1. Design data processing systems
  2. Build and operationalize data processing systems
  3. Operationalize machine learning models
  4. Ensure solution quality


Recommended Google Cloud Knowledge                                                        

  1. Designing data processing systems
  2. Selecting the appropriate storage technologies
  3. Mapping storage systems to business requirements
  4. Data modeling
  5. Tradeoffs involving latency, throughput, transactions
  6. Distributed systems
  7. Schema design
  8. Designing data pipelines
  9. Data publishing and visualization (e.g., BigQuery)
  10. Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
  11. Online (interactive) vs. batch predictions
  12. Job automation and orchestration (e.g., Cloud Composer)
  13. Choice of infrastructure
  14. System availability and fault tolerance
  15. Use of distributed systems
  16. Capacity planning
  17. Hybrid cloud and edge computing
  18. Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  19. At least once, in-order, and exactly once, etc., event processing
  20. Awareness of current state and how to migrate a design to a future state
  21. Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
  22. Validating a migration
  23. Building and operationalizing data processing systems
  24. Building and operationalizing storage systems
  25. Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
  26. Storage costs and performance
  27. Lifecycle management of data
  28. Building and operationalizing pipelines
  29. Data cleansing
  30. Batch and streaming
  31. Transformation
  32. Data acquisition and import
  33. Integrating with new data sources
  34. Building and operationalizing processing infrastructure
  35. Provisioning resources
  36. Monitoring pipelines
  37. Adjusting pipelines
  38. Testing and quality control
  39. Operationalizing machine learning models
  40. Leveraging pre-built ML models as a service
  41. ML APIs (e.g., Vision API, Speech API)
  42. Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
  43. Conversational experiences (e.g., Dialogflow)
  44. Deploying an ML pipeline
  45. Ingesting appropriate data
  46. Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
  47. Continuous evaluation
  48. Choosing the appropriate training and serving infrastructure
  49. Distributed vs. single machine
  50. Use of edge compute
  51. Hardware accelerators (e.g., GPU, TPU)
  52. Measuring, monitoring, and troubleshooting machine learning models
  53. Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
  54. Impact of dependencies of machine learning models
  55. Common sources of error (e.g., assumptions about data)
  56. Designing for security and compliance
  57. Identity and access management (e.g.,Cloud IAM)
  58. Data security (encryption, key management)
  59. Ensuring privacy (e.g., Data Loss Prevention API)
  60. Legal compliance
  61. Ensuring scalability and efficiency
  62. Building and running test suites
  63. Pipeline monitoring (e.g., Stackdriver)
  64. Assessing, troubleshooting, and improving data representations and data processing infrastructure
  65. Resizing and autoscaling resources
  66. Ensuring reliability and fidelity
  67. Performing data preparation and quality control (e.g., Cloud Dataprep)
  68. Verification and monitoring
  69. Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
  70. Choosing between ACID, idempotent, eventually consistent requirements
  71. Ensuring flexibility and portability
  72. Mapping to current and future business requirements
  73. Designing for data and application portability (e.g., multi-cloud, data residency requirements)
  74. Data staging, cataloging, and discovery


Domain 1 Design data processing systems
Domain 2 Build and operationalize data processing systems
Domain 3 Operationalize machine learning models
Domain 4 Ensure solution quality

Google Certified:

Professional Cloud Data Engineer: First Edition - 2022

  • Covers Complete & Official Exam Blueprint
  • Summarized content
  • Case study based approach
  • Ready to practice labs
  • Exam tips
  • Mind maps
  • 100% passing guarantee

No, there are no pre-requisites for this course

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