Course Information
Course Overview
Get ready for GCP Data Engineer certification with 301 real test questions and insights into best practices(Google)
Prepare for the Cloud Professional Data Engineer Certification
Master the skills to design, build, secure, and operate data platforms on G Cloud—covering ingestion, storage, processing, analytics, and ML operationalization.
Why This Certification Matters
Becoming a Professional Data Engineer proves your ability to:
Design end-to-end data architectures that meet business, reliability, latency, and cost requirements.
Build and operationalize batch and streaming pipelines using Dataflow (Apache Beam), Pub/Sub, Dataproc, and Datastream.
Implement strong governance and security with IAM, Cloud KMS/CMEK, VPC Service Controls, DLP, Dataplex, and fine-grained BigQuery access controls.
Optimize analytics with BigQuery schema design, partitioning/clustering, reservations/slots, and materialized views.
Operationalize and monitor ML with Vertex AI and BigQuery ML, including feature pipelines, versioning, and drift detection.
What You Get
251 unique, high-quality test questions.
Detailed explanations for both correct and incorrect answers.
Insights into recommended best practices with references to official documentation.
4 capstone scenario sets that mirror real-world data engineering challenges.
Guidance on leveraging services including BigQuery, Dataflow, Pub/Sub, Dataproc, Bigtable, Spanner, Datastream, Cloud Storage, Dataplex, Data Catalog, Cloud Composer, IAM, Cloud KMS, and VPC Service Controls.
Sample Question
Your company is designing a data-centric architecture. You must process and analyze 900 TB of historical .csv data in G Cloud and will continue to ingest 10 TB/day. Your current internet link is 100 Mbps.
What should you do to ensure efficient and reliable data transfer?
Options:
A. Compress and upload both the archive and daily files using gsutil -m.
B. Lease a Transfer Appliance for the 900 TB archive, then establish Dedicated Interconnect or Partner Interconnect (with Private Access for on-prem) for the 10 TB/day ingestion.
C. Use Transfer Appliance for the archive and a Cloud VPN plus gsutil -m for daily uploads.
D. Use Transfer Appliance for the archive and Cloud VPN for daily uploads.
Incorrect Answers:
A. Uploading 900 TB over 100 Mbps would take months; 10 TB/day (~1 Gbps sustained) also far exceeds available bandwidth.
C. VPN rides the public internet and will still be constrained by the 100 Mbps link; parallel gsutil doesn’t solve the bandwidth limit.
D. Same bandwidth limitation—VPN over a 100 Mbps link cannot sustain 10 TB/day.
Correct Answer:
B. Use Transfer Appliance to seed the 900 TB into Cloud Storage efficiently, then provision Dedicated or Partner Interconnect with Private Access for on-prem to reliably sustain ~1 Gbps+ for the ongoing 10 TB/day ingestion. This aligns with Google’s recommended approach for large one-time migrations plus high-volume, continuous transfers.
References:
Ready to Pass the Exam?
Test your skills, close knowledge gaps, and earn your Professional Data Engineer certification.
Course Content
- 1 section(s)
- Section 1 Practice Tests
What You’ll Learn
- Design end-to-end data platforms on Google Cloud—ingest, store, process, and serve data—meeting reliability, latency, and cost requirements.
- Select the right storage engine for a use case (BigQuery, Bigtable, Spanner, Cloud Storage, Firestore/Datastore, AlloyDB/Cloud SQL) based on access patterns, co
- Build and operationalize batch data pipelines with Dataflow (Apache Beam) and Dataproc (Spark/Hadoop), including backfills, idempotency, and fault tolerance.
- Build and operationalize streaming pipelines using Pub/Sub, Dataflow, and Datastream (CDC), applying windowing, triggers, and exactly-once/at-least-once semanti
- Orchestrate pipelines and workflows with Cloud Composer (Airflow) and Workflows
- design dependable schedules, retries, and SLA monitoring.
- Secure and govern data using IAM, service accounts, CMEK/KMS, VPC Service Controls, BigQuery row/column-level security, DLP, Dataplex, and Data Catalog.
- Optimize BigQuery with effective schema design, partitioning and clustering, materialized views, caching/BI Engine, and cost controls (reservations, slots, quot
- Operationalize ML with Vertex AI and BigQuery ML—training/serving, feature stores, pipelines, model versioning, monitoring (drift, skew), and responsible AI bas
- Monitor, log, and troubleshoot data systems using Cloud Monitoring, Cloud Logging, Error Reporting, Dataflow job metrics, and SLOs
- design for observability.
- Plan migrations and modernizations (on-prem/other clouds to GCP) using Transfer Service, BigQuery Data Transfer Service, Storage Transfer, and Datastream, inclu
Skills covered in this course
Reviews
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RRasanjana
I used this course along with Official Learning Path, and together they covered everything I needed. Passed easily.
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PPinaki Cal
Great course, the explanation are clear and straight to the point
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AAbbi Las
Good mix of questions with detailed explanations. Really helped me get ready for the certification exam
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FFrancesco Mugo
Really good practice set! The question style was very close to the real exam, and it helped me a lot