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Download Google : GCP-DE Questions & Answers as PDF & Test Software

Last Updated: Jul 26, 2026

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Google GCP-DE Exam Syllabus Topics:

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

Google Data Engineer Sample Questions:

1. By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A) Windows at every 100 MB of data
B) Single, Global Window
C) Windows at every 1 minute
D) Windows at every 10 minutes


2. 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) Change the zone of your Cloud Dataflow pipeline to run in us-central1
D) Use a larger instance type for your Cloud Dataflow workers
E) 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
F) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
G) Create a temporary table in Cloud Spanner that will act as a buffer for new data


3. MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A) Rowkey: device_idColumn data: date, data_point
B) Rowkey: data_pointColumn data: device_id, date
C) Rowkey: dateColumn data: device_id, data_point
D) Rowkey: date#data_pointColumn data: device_id
E) Rowkey: date#device_idColumn data: data_point


4. The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A) conditional node
B) application node
C) master node
D) worker node


5. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

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


Solutions:

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

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