Free demo, safe international payment, instant delivery, a year of free updates, and 24/7 aftersales agents who keep your privacy absolutely: TestPassKing is the considerate choice for 2026 GCP-DE candidates preparing for Google Data Engineer.
Google GCP-DE Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Certification Exam |
| Exam Number: | PDE |
| Real Exam Qty: | Approximately 50–60 |
| Exam Format: | Proctored online or onsite (Kryterion Webassessor), Multiple select, Multiple choice |
| Passing Score: | Not publicly disclosed |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Available Languages: | English, Japanese |
| Related Certifications: | Google Cloud Associate Cloud Engineer Google Cloud Professional Machine Learning Engineer |
| Exam Duration: | 120 minutes |
| Recommended Training: | Coursera - Google Cloud Data Engineering Professional Certificate Google Cloud Skills Boost - Data Engineering Path |
| Exam Registration: | Kryterion Webassessor Portal Google Cloud Certification Registration |
| Sample Questions: |  |
| Exam Way: | Online proctored or test center (Kryterion Webassessor) |
| Pre Condition: | No mandatory prerequisites, but Associate Cloud Engineer or equivalent experience recommended |
| Official Syllabus URL: | https://cloud.google.com/certification/data-engineer |
Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 20% | - Storage and data modeling
|
| Operationalizing data and ML pipelines | 30% | - Pipeline automation and orchestration
|
| Maintaining and optimizing data and ML solutions | 20% | - Machine learning integration
|
| Building and operationalizing data processing systems | 30% | - Data ingestion and transformation
|
GCP-DE Exam: Your Questions, Our Promises
Google Data Engineer is an official Google Cloud exam, registered under the code GCP-DE. Passing it earns the Professional Data Engineer certification at the Professional level. It also connects to Google Cloud Associate Cloud Engineer, Google Cloud Professional Machine Learning Engineer. In a fast-developed industry, this certificate is a promise to your career and your next promotion.
No mandatory prerequisites, but Associate Cloud Engineer or equivalent experience recommended
Vendor policies do evolve, so confirm the current conditions before you register on the official exam page.
The Google Data Engineer exam contains Approximately 50–60 questions within 120 minutes. Former customers share a consistent secret: two or three regular hours of daily practice beat sporadic cramming. The TestPassKing engine covers every important test point, so a steady routine builds both knowledge and the pacing the clock demands.
Passing Google Data Engineer requires Not publicly disclosed, and the official fee is $200 USD. Retakes bill the full $200 USD again, so treat readiness as something to verify, not assume: when your TestPassKing practice scores clear the requirement day after day, you are ready to book.
Registration for Google Data Engineer runs through the official channels below.
One scheduling detail: the exam is delivered Online proctored or test center (Kryterion Webassessor).
Yes, Google Cloud recommends the following training for Google Data Engineer candidates.
- Google Cloud Skills Boost - Data Engineering Path
- Coursera - Google Cloud Data Engineering Professional Certificate
Whichever training you follow, practice daily with the 77 questions in the TestPassKing GCP-DE package; regular hours with real exam-style items are what turn preparation into a pass.
Google Data Engineer is divided into 4 official domains, led by Maintaining and optimizing data and ML solutions (20%), Building and operationalizing data processing systems (30%), and Operationalizing data and ML pipelines (30%). The full outline is above; while details shift over time, the main test points stay steady, and we have already sorted them for you.
Yes and yes. If you are a little suspicious, download the free demo of the Google Data Engineer questions and check the material before deciding. After purchase, updates are free for 365 days, with new versions sent to you as soon as test points change; after expiry, extending the update service costs 50% of the regular price.
We promise it in writing: a 100% money-back guarantee under defined conditions. Take the Google Data Engineer exam within 60 days of purchase; if you fail, claim a full refund according to your transcript by submitting a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, processed within 7 days. The exam must match your product, candidate and payer names must match, and attempts within 3 days of purchase, unused downloads, free materials, and expired orders are excluded. Alternatively, switch freely to other exam material: two equivalent products, free, with updates retained on your original purchase.
Delivery is instant: files unlock for download at payment and are emailed within one minute, so there is no wasted time between ordering and studying. If nothing arrives within 2 hours, check spam and contact our 24/7 aftersales agents. Installation is unlimited.
Google Data Engineer Sample Questions:
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- C. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- D. Store the data in Google Cloud Datastor
- E. Store the data in a file in a regional Google Cloud Storage bucke
- F. Load the data every 30 minutes into a new partitioned table in BigQuery.
Correct Answer: F 🗳️
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?
- A. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
- B. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- C. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
- D. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
- E. Merge the transformed tables together with a SQL query
- F. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
Correct Answer: C 🗳️
When a Cloud Bigtable node fails, is lost.
- A. the time dimension
- B. the last transaction
- C. all data
- D. no data
Correct Answer: D 🗳️
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You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?
- A. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
- B. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
- C. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
- D. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
Correct Answer: B 🗳️
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
- A. BigQuery
- B. Cloud Dataflow
- C. Cloud Bigtable
- D. Cloud Datastore
Correct Answer: A 🗳️
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