Exam Name: Data Engineer
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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Updated: Oct 09, 2026
No. of Questions: 77 Questions & Answers with Testing Engine
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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Designing data processing systems | 24% | - Designing data pipelines
|
| Topic 2: Ingesting and processing data | 25% | - Transforming data
|
| Topic 3: Maintaining and automating data workloads | 18% | - Operationalizing workloads
|
| Topic 4: Preparing data for analysis and machine learning | 13% | - Enabling data analysis
|
| Topic 5: Storing and managing data | 20% | - Implementing storage solutions
|
Google Data Engineer Sample Questions:
Which of these is not a supported method of putting data into a partitioned table?
- A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
- B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- C. Create a partitioned table and stream new records to it every day.
- D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
Correct Answer: D 🗳️
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You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use the WHERE_PARTITIONTIME pseudo column
- B. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- C. Use the TABLE_DATE_RANGE function
- D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
Correct Answer: C 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud Spanner for storag
- B. Add secondary indexes to support query patterns.
- C. Add secondary indexes to support query patterns.
- D. Use Cloud Spanner for storag
- E. Use Cloud Dataflow to transform data to support query patterns.
- F. Use Cloud SQL for storag
- G. Use Cloud Dataflow to transform data to support query patterns.
- H. Use Cloud SQL for storag
Correct Answer: E 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?
- A. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
- B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
- C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
- D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
Correct Answer: A 🗳️
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
- A. Use Cloud Vision AutoML with the existing dataset.
- B. Train your own image recognition model leveraging transfer learning techniques.
- C. Use Cloud Vision AutoML, but reduce your dataset twice.
- D. Use Cloud Vision API by providing custom labels as recognition hints.
Correct Answer: A 🗳️
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