Google GCP-DE : Data Engineer

  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Aug 05, 2026
  • Q & A: 77 Questions and Answers

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality20%-25%- Data quality management
  • 1. Implement data governance
  • 2. Validate and clean data
  • 3. Maintain metadata and lineage
- Security and compliance
  • 1. Manage IAM and access control
  • 2. Protect sensitive data
  • 3. Implement encryption and compliance controls
Topic 2: Managing and optimizing solutions20%-25%- Reliability and scalability
  • 1. Design highly available systems
  • 2. Implement disaster recovery strategies
  • 3. Scale data workloads efficiently
- Managing resources and costs
  • 1. Tune query and pipeline performance
  • 2. Control operational costs
  • 3. Optimize storage and compute usage
Topic 3: Building and operationalizing data processing systems28%-33%- Building data processing systems
  • 1. Implement batch processing systems
  • 2. Implement streaming data systems
  • 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- Operationalizing systems
  • 1. Manage logging and alerting
  • 2. Monitor data pipelines and workloads
  • 3. Implement fault tolerance and recovery
Topic 4: Designing data processing systems22%-27%- Designing data pipelines
  • 1. Optimize pipeline performance and cost
  • 2. Build ETL and ELT pipelines
  • 3. Design orchestration workflows
- Designing for data ingestion
  • 1. Design scalable and reliable ingestion architecture
  • 2. Design batch and streaming ingestion solutions
  • 3. Select appropriate storage systems

Google Data Engineer Sample Questions:

1. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Use the diagnose command to generate an operational output archiv
B) Monitor CPU utilization for the cluste
C) Use Cloud Dataflow to run your transformation
D) Use Cloud Dataproc to run your transformation
E) Use Cloud Dataflow to run your transformation
F) Monitor the total execution time for a sampling of job
G) Configure the job to use non-default Compute Engine machine types when needed.
H) Monitor the job system lag with Stackdrive
I) Use Cloud Dataproc to run your transformation
J) Locate the bottleneck and adjust cluster resources.
K) Resize the number of worker nodes in your cluster via the command line.
L) Use the default autoscaling setting for worker instances.


2. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A) Cloud Bigtable
B) Cloud SQL
C) BigQuery
D) Cloud Datastore


3. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
B) Increase the number of max workers
C) Use a larger instance type for your Cloud Dataflow workers
D) Change the zone of your Cloud Dataflow pipeline to run in us-central1
E) Create a temporary table in Cloud Spanner that will act as a buffer for new data
F) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
G) Create a temporary table in Cloud Bigtable that will act as a buffer for new data


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) Assemble 1,000 files into a tape archive (TAR) fil
C) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
D) Introduce data compression for each file to increase the rate file of file transfer.
E) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
F) 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.


5. You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A) Source API
B) Sink API
C) ParDo
D) Data extraction


Solutions:

Question # 1
Answer: B
Question # 2
Answer: B
Question # 3
Answer: C,F
Question # 4
Answer: A,E
Question # 5
Answer: C

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