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Google GCP-DE

GCP-DE

Exam Code: GCP-DE

Exam Name: Data Engineer

Updated: Aug 26, 2026

Q&A Number: 77 Q&As

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About Google GCP-DE Exam Questions and Answers

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems24%- Designing data pipelines
  • 1. Defining architecture for batch and streaming processing
  • 2. Designing for data transformation and enrichment
  • 3. Planning for data migration and integration
- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Designing for reliability, scalability, and efficiency
  • 3. Selecting appropriate storage solutions
Topic 2: Maintaining and automating data workloads18%- Operationalizing workloads
  • 1. Orchestrating and scheduling jobs
  • 2. Ensuring reliability and recoverability
  • 3. Monitoring and troubleshooting pipelines
- Automation and optimization
  • 1. Optimizing resource usage and costs
  • 2. Automating deployment and management
Topic 3: Ingesting and processing data25%- Building data pipelines
  • 1. Handling data quality and consistency
  • 2. Implementing ingestion mechanisms
  • 3. Developing batch and streaming workflows
- Transforming data
  • 1. Applying data processing logic
  • 2. Optimizing transformations for performance
  • 3. Managing schema evolution
Topic 4: Preparing data for analysis and machine learning13%- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving
- Enabling data analysis
  • 1. Optimizing query performance
  • 2. Preparing datasets for querying and reporting
  • 3. Implementing data sharing and governance
Topic 5: Storing and managing data20%- Implementing storage solutions
  • 1. Managing data lifecycle and retention
  • 2. Using data storage services appropriately
  • 3. Designing data warehouses and data lakes
- Optimizing storage performance and cost
  • 1. Implementing cost optimization strategies
  • 2. Configuring storage for access patterns

Google Data Engineer Sample Questions:

Question 1

Cloud Dataproc is a managed Apache Hadoop and Apache service.

A. Ignite
B. Spark
C. Fire
D. Blaze


Question 2

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. Pig
B. Spark
C. YARN
D. Hive


Question 3

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Include multiple time series values within the row key
B. Keep your row key reasonably short
C. Keep your row key as long as the field permits
D. Keep the row keep as an 8 bit integer


Question 4

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
B. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C. Assemble 1,000 files into a tape archive (TAR) fil
D. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
F. Introduce data compression for each file to increase the rate file of file transfer.


Question 5

What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use a partitioned table; 2) Join tables into one table
B. 1) Use nested repeated fields; 2) Use a partitioned table
C. 1) Split table into multiple tables; 2) Use a partitioned table
D. 1) Join tables into one table; 2) Use nested repeated fields


Solutions:

Question 1
Answer: B
Question 2
Answer: A,B,D
Question 3
Answer: B
Question 4
Answer: A,B
Question 5
Answer: D

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