Skeptical buyers are our favorite kind. GetValidTest publishes a free demo of the Google Data Engineer questions so you can cross-examine the GCP-DE material yourself before spending a cent.
Google GCP-DE Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | GCP-DE / PDE |
| Exam Format: | Multiple select, Multiple choice |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 40 - 50 |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Available Languages: | English, Japanese |
| Related Certifications: | Google Cloud Professional Data Analyst Google Cloud Professional Cloud Architect Google Cloud Associate Cloud Engineer |
| Exam Price: | $200 USD (plus tax where applicable) |
| Recommended Training: | Google Cloud Professional Data Engineer Learning Path Official Exam Guide |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: |  |
| Exam Way: | Online-proctored or onsite-proctored at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, 1+ year working with Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Maintaining and automating data workloads | 18% | - Operationalizing workloads
|
| Topic 2: Storing and managing data | 20% | - Implementing storage solutions
|
| Topic 3: Ingesting and processing data | 25% | - Transforming data
|
| Topic 4: Designing data processing systems | 24% | - Designing data pipelines
|
| Topic 5: Preparing data for analysis and machine learning | 13% | - Preparing data for ML
|
What You Need to Know About the GCP-DE Exam
Google Data Engineer is an official certification exam from Google Cloud, identified by the exam code GCP-DE. Pass it and you hold the Professional Data Engineer certification, which sits at the Professional level. It also feeds into related credentials, including Google Cloud Professional Cloud Architect, Google Cloud Professional Data Analyst, Google Cloud Associate Cloud Engineer. In practical terms, this certification tells employers your skills have been measured against the vendor's own standard, which is why certified candidates tend to stand out in hiring and promotion decisions.
Expect 40 - 50 questions within 120 minutes on the Google Data Engineer exam. Numbers aside, the real challenge is rhythm: lingering too long on one item quietly steals time from three others. Build the habit of answering, flagging, and moving on, then rehearse the full experience under a countdown with the GetValidTest test engine until finishing comfortably early becomes your norm.
Registration for Google Data Engineer officially runs $200 USD (plus tax where applicable), with Not publicly disclosed (scaled scoring system) required to pass. Worth knowing before you book: retakes are billed at the same full $200 USD (plus tax where applicable), so a failed attempt doubles your spend. The smarter sequence is to measure yourself first. When your scores on the GetValidTest practice tests clear the passing line with room to spare, consistently, that is your signal to schedule the real thing.
No mandatory prerequisites; recommended 3+ years industry experience, 1+ year working with Google Cloud data solutions
Because vendors update their policies without much fanfare, verify the latest entry requirements before you book using the official exam page.
Booking for the Google Data Engineer exam goes through the vendor's official registration channels.
When you choose your slot, note that the exam is delivered Online-proctored or onsite-proctored at authorized test centers.
Google Cloud lists the following recommended training for Google Data Engineer candidates.
Courses teach the concepts; repetition makes them exam-ready. Round out whichever training you pick with the 77 practice questions from GetValidTest, and each topic stops being theory and starts being points.
Yes, and we encourage it. Download the free PDF demo of the Google Data Engineer questions from GetValidTest and evaluate the quality on your own terms. After you buy, every update for the next 365 days is free, and when that window closes you can extend your update service at half price.
A 100% money-back guarantee applies, governed by clear conditions. If you sit the Google Data Engineer exam within 60 days of purchase and fail, you can claim a full refund, as long as the exam corresponds to your product. Attempts made within 3 days of purchase are ineligible, as are products you downloaded but never used, free materials, and expired orders; the name on the exam record must match the payer's name. File your claim with a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and it is settled within 7 days. Rather have a replacement than a refund? You may exchange for two other exam products of equal value, free, and your original product keeps its update service.
Delivery is essentially instantaneous: files unlock for download at payment and arrive in your email within one minute. Some vendors make you wait a day; if 2 hours pass here without delivery, check spam and contact customer service. Install on as many computers as you like, with no restrictions.
The Google Data Engineer blueprint spans 5 domains. The ones carrying the most weight include Maintaining and automating data workloads (18%), Ingesting and processing data (25%), and Designing data processing systems (24%). The complete domain-by-domain breakdown is in the exam topics section above; treat it as your master checklist and let the weights tell you where your study hours earn the most.
Google Data Engineer Sample Questions:
Which of the following is NOT true about Dataflow pipelines?
- A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
- B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
- C. Dataflow pipelines can consume data from other Google Cloud services
- D. Dataflow pipelines can be programmed in Java
Correct Answer: A 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
- A. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
- B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
- C. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
- D. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- E. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
Correct Answer: C 🗳️
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .
- A. conditional node
- B. master node
- C. application node
- D. worker node
Correct Answer: B 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
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. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
- B. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
- C. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
- D. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
Correct Answer: C 🗳️
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
- A. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
- B. Add a SideInput that returns a Boolean if the element is corrupt.
- C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
- D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
Correct Answer: C 🗳️
Viewing page 1 out of 16 pages.
Viewing questions 1-5 out of 77 questions.
