Google GCP-DE : Data Engineer
- Exam Code: GCP-DE
- Exam Name: Data Engineer
- Updated: Oct 08, 2026
- Q & A: 77 Questions and Answers
About Google GCP-DE Exam
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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing and optimizing solutions | 20%-25% | - Reliability and scalability
|
| Topic 2: Designing data processing systems | 22%-27% | - Designing for data ingestion
|
| Topic 3: Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
|
| Topic 4: Ensuring solution quality | 20%-25% | - Data quality management
|
Google Data Engineer Sample Questions:
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?
- A. Deploy a Cloud Dataproc cluste
- B. Change references in scripts from hdfs:// to gs://
- C. Use a standard persistent disk and 50% preemptible worker
- D. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
- E. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- F. Deploy a Cloud Dataproc cluste
- G. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
- H. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- I. Install the Cloud Storage connector, and store the data in Cloud Storag
- J. Change references in scripts from hdfs:// to gs://
- K. Use an SSD persistent disk and 50% preemptible worker
Correct Answer: F 🗳️
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?
- A. An event time trigger
- B. The with Allowed Lateness method
- C. A processing time trigger
- D. An hourly watermark
Correct Answer: C 🗳️
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You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
- A. Python using MapReduce
- B. HiveQL using Hive
- C. PigLatin using Pig
- D. Java using MapReduce
Correct Answer: A 🗳️
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- A. Create separate views to cover each month, and query from these views
- B. Convert all daily log tables into date-partitioned tables
- C. Enable query caching so you can cache data from previous months
- D. Convert the sharded tables into a single partitioned table
Correct Answer: B 🗳️
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?
- A. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
- C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
Correct Answer: C 🗳️
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