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Updated: Aug 06, 2026

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GCP-DE Practice Q&A's

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Google GCP-DE Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Professional Data Engineer
Exam Number:GCP-DE
Related Certifications:Google Cloud Certified Professional Data Engineer
Certificate Validity Period:2 years
Passing Score:Not publicly disclosed
Exam Duration:120 minutes
Real Exam Qty:50-60
Exam Price:$200 USD
Available Languages:English, Japanese
Exam Format:Multiple choice, Multiple select
Sample Questions:Google GCP-DE Sample Questions
Exam Way:Online proctored or onsite testing center
Pre Condition:No formal prerequisites. Google recommends 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

Google GCP-DE Exam Syllabus Topics:

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

Google Data Engineer Sample Questions:

1. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
B) Preserve the structure of the data as much as possible.
C) Use BigQuery UPDATE to further reduce the size of the dataset.
D) Denormalize the data as must as possible.
E) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
F) Use BigQuery'ssupport for external data sources to query.


2. What are the minimum permissions needed for a service account used with Google Dataproc?

A) Write to Google Cloud Storage; read to Google Cloud Logging
B) Execute to Google Cloud Storage; execute to Google Cloud Logging
C) Execute to Google Cloud Storage; write to Google Cloud Logging
D) Read and write to Google Cloud Storage; write to Google Cloud Logging


3. Which of these sources can you not load data into BigQuery from?

A) File upload
B) Google Cloud SQL
C) Google Drive
D) Google Cloud Storage


4. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
B) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
C) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D) Add a SideInput that returns a Boolean if the element is corrupt.


5. You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A) Pipeline
B) PCollection
C) Transform
D) Sink API


Solutions:

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

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