Google GCP-DE : Data Engineer

  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Oct 06, 2026
  • Q & A: 77 Questions and Answers

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Google GCP-DE Exam Syllabus Topics:

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

Google Data Engineer Sample Questions:

Question #1

Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

  • A. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
  • B. Use K-means Clustering to detect faces in the pixels.
  • C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
  • D. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

  • A. Remove the features that have null values for more than 50% of the training records.
  • B. Combine highly co-dependent features into one representative feature.
  • C. Instead of feeding in each feature individually, average their values in batches of 3.
  • D. Eliminate features that are highly correlated to the output labels.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

  • A. Cloud SQL
  • B. Cloud Bigtable
  • C. Cloud Spanner
  • D. Cloud Datastore
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

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 temporary table in Cloud Bigtable that will act as a buffer for new data
  • B. Change the zone of your Cloud Dataflow pipeline to run in us-central1
  • C. Create a temporary table in Cloud Spanner that will act as a buffer for new data
  • D. 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
  • E. Increase the number of max workers
  • F. Use a larger instance type for your Cloud Dataflow workers
  • G. 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
Reveal Solution  Discussion  0

Correct Answer: D,F  🗳️

Question #5

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. Java using MapReduce
  • D. PigLatin using Pig
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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