Google Data Engineer - GCP-DE Valid Dumps

Updated: Aug 08, 2026
Q & A: 77 Questions and Answers

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Google GCP-DE Exam Syllabus Topics:

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

Google Data Engineer Sample Questions:

1. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Add secondary indexes to support query patterns.
B) Use Cloud Spanner for storag
C) Use Cloud Dataflow to transform data to support query patterns.
D) Use Cloud Spanner for storag
E) Use Cloud SQL for storag
F) Add secondary indexes to support query patterns.
G) Use Cloud SQL for storag
H) Use Cloud Dataflow to transform data to support query patterns.


2. 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) Cloud SQL
B) BigQuery
C) Cloud Datastore
D) Cloud Bigtable


3. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A) Install the Cloud Storage connector, and store the data in Cloud Storag
B) Deploy a Cloud Dataproc cluste
C) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D) Change references in scripts from hdfs:// to gs://
E) Deploy a Cloud Dataproc cluste
F) Use an SSD persistent disk and 50% preemptible worker
G) Use a standard persistent disk and 50% preemptible worker
H) Change references in scripts from hdfs:// to gs://
I) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
J) Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
K) Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF


4. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
B) Create an authorized view in BigQuery to restrict access to tables with sensitive data.
C) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
D) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
E) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.


5. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A) Preemptible workers cannot use persistent disk.
B) If a preemptible worker is reclaimed, then a replacement worker must be added manually.
C) Preemptible workers cannot store data.
D) A Dataproc cluster cannot have only preemptible workers.


Solutions:

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

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