Certification exams change, and study material has to keep up. The Professional-Data-Engineer (Google Certified Professional Data Engineer) question bank at Actual4Exams is reviewed continuously and comes with 365 days of free updates, so your 2026 preparation always reflects the current exam.
Google Professional-Data-Engineer Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer |
| Exam Number: | Professional-Data-Engineer |
| Passing Score: | Not officially published (estimated ~80%) |
| Exam Duration: | 120 minutes |
| Related Certifications: | Google Cloud Certified Professional Data Engineer |
| Real Exam Qty: | 50-60 |
| Available Languages: | English, Japanese |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-select, Multiple-choice |
| Sample Questions: |  |
| Exam Way: | Online (remote proctored) or at a testing center (Kryterion) |
| Pre Condition: | No mandatory prerequisites. Recommended: 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 Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Maintaining and automating data workloads (~15% of the exam) | 15% | - Automating data processes
|
| Topic 2: Storing the data (~20% of the exam) | 20% | - Selecting storage systems
|
| Topic 3: Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
|
| Topic 4: Designing data processing systems (~30% of the exam) | 30% | - Selecting appropriate storage technologies
|
| Topic 5: Ingesting and processing the data (~20% of the exam) | 20% | - Deploying and operationalizing the pipelines
|
Google Certified Professional Data Engineer (Professional-Data-Engineer) — Questions Candidates Actually Ask
The Professional-Data-Engineer exam, officially known as Google Certified Professional Data Engineer, is the Google test that leads to the Google Cloud Certified certification at the Professional level. Passing it validates the skills employers expect from a certified professional. It is also associated with related credentials such as Google Cloud Certified Professional Data Engineer.
The Professional-Data-Engineer exam contains 50-60 questions, and you have 120 minutes to complete them. Work out your per-question pace before test day, and flag slow items instead of stalling on them — time pressure, not knowledge, sinks many first attempts. Timed mock exams in the Actual4Exams test engines are the most reliable way to build that rhythm.
The passing score for the Professional-Data-Engineer exam is Not officially published (estimated ~80%), and the official registration fee is $200 USD. If you miss the mark, a retake means paying the full fee again, so book your seat only when you are ready. A practical benchmark: score consistently above the passing line on timed practice tests before scheduling the real exam.
No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Entry requirements can change, so confirm the latest conditions on the official exam page: https://cloud.google.com/learn/certification/data-engineer.
Yes. A free PDF demo of the Google Certified Professional Data Engineer questions is available, so you can check the question style and answer quality before you pay. Every purchase also includes 365 days of free updates, and if the product expires you can renew the update service at a 50% discount from your member zone.
If you take the corresponding Professional-Data-Engineer exam within 60 days of purchase and do not pass, you can apply for a full refund under the 100% Money Back Guarantee: submit a scan of your enrollment slip and your official Score Report (PDF) within 2 days of the exam date, and the claim is processed within 7 days. Attempts made within 3 days of purchase, downloads without an actual exam attempt, free materials, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your purchase for two free products of equal value and keep the update service on your original product. As for delivery, the files are available for instant download and are also emailed to you within one minute of payment — if nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you can install the product on.
The official Google Certified Professional Data Engineer outline is organized into 5 domains. The first three are:
- Preparing and using data for analysis (~15% of the exam) — 15% of the exam
- Storing the data (~20% of the exam) — 20% of the exam
- Ingesting and processing the data (~20% of the exam) — 20% of the exam
See the complete exam topics section above for the full outline and the weighting of every domain.
Google Certified Professional Data Engineer Sample Questions:
Question 1
You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?
A. Assign the dataflow.admin role to the service account that executes the Dataflow pipeline.
B. Assign the compute.networkUser role to the Dataflow service agent.
C. Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.
D. Assign the dataflow.admin role to the Dataflow service agent.
Question 2
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You need to compose visualizations for operations teams with the following requirements:
* The report must include telemetry data from all 50,000 installations for the most resent 6 weeks (sampling once every minute).
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
Which approach meets the requirements?
A. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
B. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
D. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
Question 3
You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table.
This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?
A. Create a BigQuery reservation for the job.
B. Create a BigQuery reservation for the service account running the job.
C. Create a BigQuery reservation for the dataset.
D. Create a BigQuery reservation for the project.
Question 4
You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern. Which service do you select for storing and serving your data?
A. Cloud Spanner
B. Cloud Bigtable
C. Cloud Firestore
D. Cloud SQL
Question 5
Which of the following is not true about Dataflow pipelines?
A. Pipelines are a set of operations
B. Pipelines represent a data processing job
C. Pipelines represent a directed graph of steps
D. Pipelines can share data between instances
Solutions:
| Question 1 Answer: C | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: D |
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