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Practice Exams | Professional Data Engineer(Google GCP)
Rating: 4.6 out of 5(38 ratings)
794 students

Practice Exams | Professional Data Engineer(Google GCP)

Get ready for GCP Data Engineer certification with 301 real test questions and insights into best practices(Google)
Created byNadiya Tsymbal
Last updated 3/2026
English

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

Included in This Course

301 questions

  • Test 1 | Test 1 Professional Data Engineer60 questions
  • Test 2 | Test 2 Professional Data Engineer60 questions
  • Test 3 | Test 3 Professional Data Engineer60 questions
  • Test 4 | Test 4 Professional Data Engineer60 questions
  • Test 5 | Test 5 Professional Data Engineer60 questions
  • Bonus Lecture1 question

Description

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.

Who this course is for:

  • Data engineers preparing for the Google Professional Data Engineer certification who want realistic, scenario-based practice.
  • Analytics engineers and BI developers moving workloads to BigQuery and modernizing ELT on Google Cloud.
  • Software/backend engineers building data-intensive and event-driven services with Pub/Sub, Dataflow, and Dataproc.
  • Machine learning engineers and data scientists who need reliable feature pipelines and production data workflows for Vertex AI / BigQuery ML.
  • Data platform and infrastructure engineers responsible for orchestration, reliability, and observability using Cloud Composer, Workflows, and Cloud Monitoring/Logging.
  • Architects and tech leads designing secure, compliant, multi-region data platforms with strong governance (IAM, DLP, Dataplex).
  • Teams seeking organization-wide G Cloud upskilling and certifications to unlock partner benefits and standardize best practices.
  • Learners transitioning from SQL/BI roles to cloud data engineering who prefer hands-on, exam-style preparation over theory-heavy lectures.

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