Google GCP-DE : Data Engineer

GCP-DE real exams

Exam Code: GCP-DE

Exam Name: Data Engineer

Updated: Oct 25, 2026

Q & A: 77 Questions and Answers

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Google GCP-DE Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Professional Data Engineer
Exam Number:GCP-DE
Exam Format:Multiple select, Multiple choice
Exam Price:$200 USD
Certificate Validity Period:2 years
Exam Duration:120 minutes
Available Languages:English, Japanese
Real Exam Qty:50-60
Passing Score:Not publicly disclosed
Related Certifications:Google Cloud Certified Professional Data Engineer
Sample Questions:Free Download real GCP-DE exam dumps
Exam Way:Online proctored or onsite testing center
Pre Condition:No formal prerequisites. Google recommends 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Control operational costs
  • 2. Tune query and pipeline performance
  • 3. Optimize storage and compute usage
- Reliability and scalability
  • 1. Design highly available systems
  • 2. Implement disaster recovery strategies
  • 3. Scale data workloads efficiently
Topic 2: Ensuring solution quality20%-25%- Data quality management
  • 1. Maintain metadata and lineage
  • 2. Validate and clean data
  • 3. Implement data governance
- Security and compliance
  • 1. Implement encryption and compliance controls
  • 2. Protect sensitive data
  • 3. Manage IAM and access control
Topic 3: Designing data processing systems22%-27%- Designing data pipelines
  • 1. Build ETL and ELT pipelines
  • 2. Optimize pipeline performance and cost
  • 3. Design orchestration workflows
- Designing for data ingestion
  • 1. Design scalable and reliable ingestion architecture
  • 2. Select appropriate storage systems
  • 3. Design batch and streaming ingestion solutions
Topic 4: Building and operationalizing data processing systems28%-33%- Operationalizing systems
  • 1. Implement fault tolerance and recovery
  • 2. Monitor data pipelines and workloads
  • 3. Manage logging and alerting
- Building data processing systems
  • 1. Implement batch processing systems
  • 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 3. Implement streaming data systems

The GCP-DE Details Every Candidate Should Check

As of the latest information, the GCP-DE exam requires a passing score of Not publicly disclosed and a registration fee of $200 USD. Google may revise either figure, so confirm both on the official site before scheduling.

Google lists these prerequisites for the Google Data Engineer: No formal prerequisites. Google recommends 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud..

Check the current requirements on the official certification page before booking.

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Smart preparation means current materials, verified answers, and a schedule that fits real life. The GCP-DE bank is written by a team of Google experts and certified trainers, and our colleagues check it continuously for updates so accuracy never drifts. You practice in your spare time — no classes required — with expert-verified answers across the Google Data Engineer objectives. Before purchase, a free demo is downloadable for reference; after purchase, updates arrive by email for 365 days and 24/7 customer assisting handles any question. That combination is the smart way.

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Per current exam information, the GCP-DE exam contains 50-60 questions within a 120 minutes-minute limit. Practicing under those exact conditions removes the format shock on exam day.

The Google Data Engineer blueprint centers on these main domains:

  • Managing and optimizing solutions (20%-25%)
  • Ensuring solution quality (20%-25%)
  • Designing data processing systems (22%-27%)

The full official outline continues with further domains; the question bank covers them all.

Google Data Engineer Sample Questions:

Question #1

You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

  • A. Call the Cloud Natural Language API from your applicatio
  • B. Call the model from your application and process the results as labels.
  • C. Call the Cloud Natural Language API from your applicatio
  • D. Deploy the model using Cloud Machine Learning Engin
  • E. Deploy the model using a KubernetesEngine cluste
  • F. Call the model from your application and process the results as labels.
  • G. Build and train a text classification model using TensorFlo
  • H. Process the generated Sentiment Analysis as labels.
  • I. Build and train a text classification model using TensorFlo
  • J. Process the generated Entity Analysis as labels.
Reveal Solution  Discussion  0

Correct Answer: J  🗳️

Question #2

Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

  • A. Add ", UNNEST(person)" before the WHERE clause.
  • B. Add ", UNNEST(city)" before the WHERE clause.
  • C. Change "person" to "person.city".
  • D. Change "person" to "city.person".
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #3

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

  • A. Keep your row key as long as the field permits
  • B. Keep your row key reasonably short
  • C. Keep the row keep as an 8 bit integer
  • D. Include multiple time series values within the row key
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

When a Cloud Bigtable node fails, is lost.

  • A. the last transaction
  • B. all data
  • C. the time dimension
  • D. no data
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #5

You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

  • A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
  • B. Create a table on Cloud Spanner, and insert and delete rows with the job information
  • C. Create a table on Cloud SQL, and insert and delete rows with the job information
  • D. Create an API using App Engine to receive and send messages to the applications
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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