A high-quality, authoritative exam is a long and tough climb — especially when your calendar is already full. TestValid respects that reality: the GCP-DE bank delivers 77 expert-verified practice questions covering the Google Data Engineer objectives, built for professionals short on time.
Google GCP-DE Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Professional Data Engineer |
| Exam Number: | GCP-DE |
| Certificate Validity Period: | 2 years |
| Passing Score: | Not publicly disclosed |
| Exam Format: | Multiple choice, Multiple select |
| Real Exam Qty: | 50-60 |
| Exam Duration: | 120 minutes |
| Related Certifications: | Google Cloud Certified Professional Data Engineer |
| Available Languages: | Japanese, English |
| Exam Price: | $200 USD |
| Sample Questions: |  |
| 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:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring solution quality | 20%-25% | - Security and compliance
|
| Topic 2: Designing data processing systems | 22%-27% | - Designing for data ingestion
|
| Topic 3: Building and operationalizing data processing systems | 28%-33% | - Operationalizing systems
|
| Topic 4: Managing and optimizing solutions | 20%-25% | - Managing resources and costs
|
GCP-DE Exam Facts for Busy Professionals
According to the latest exam information, the GCP-DE exam contains 50-60 questions and runs for 120 minutes minutes. Simulating that exact limit in practice builds the pacing a tough exam demands.
The Google Data Engineer blueprint centers on these domains:
- Designing data processing systems (22%-27%)
- Managing and optimizing solutions (20%-25%)
- Ensuring solution quality (20%-25%)
Further domains complete the official outline — the question bank covers every one.
Clear terms, no runaround. If you fail the corresponding exam within 60 days of purchase, send us a scanned copy of your enrollment slip and your official Score Report PDF within two days of the exam date; verified claims are refunded in full within seven days. Exclusions: exams taken within three days of purchase, candidate names that do not match the payer, and free or expired products. Prefer to switch tracks? Exchange your product for two others of equal value at no cost.
A team of IT experts and certified trainers with rich experience in the Google Data Engineer field writes it — and keeps it honest. Every answer is expert-verified, the content is checked continuously for updates, and each new GCP-DE version is emailed to you free for 365 days. Compared with training institutions, the bank is affordable and self-paced; compared with guesswork, it is systematic. Download the free demo first, and if anything goes wrong — even a simple downloading problem — 24/7 customer assistance is one message away.
Google states the following 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..
Confirm the current requirements on the official certification page before you register.
Currently, the GCP-DE exam requires a passing score of Not publicly disclosed, with a registration fee of $200 USD. Google sets both figures, so verify the latest on the official site before scheduling.
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Google Data Engineer Sample Questions:
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 GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
- C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
- D. Add a SideInput that returns a Boolean if the element is corrupt.
Correct Answer: C 🗳️
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
- A. Cloud Datastore
- B. Cloud Bigtable
- C. Cloud Dataflow
- D. BigQuery
Correct Answer: D 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Create a cron schedule in Cloud Dataprep.
- B. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
- C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
- D. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
Correct Answer: D 🗳️
Cloud Dataproc is a managed Apache Hadoop and Apache service.
- A. Blaze
- B. Ignite
- C. Fire
- D. Spark
Correct Answer: D 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)
- A. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
- B. Mount the Hive tables locally.
- C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
- D. Mount the Hive tables locally.
- E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
- F. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
- G. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
- H. Replicate external Hive tables to the native ones.
- I. Then run the Hadoop utility to copy them do HDF
- J. Mount the Hive tables from HDFS.
- K. Load the ORC files into BigQuer
- L. Replicate external Hive tables to the native ones.
Correct Answer: A,B 🗳️
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