Pass Guaranteed Quiz 2026 Google GCP-DE: Data Engineer–High Pass-Rate Exam Lab Questions

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q57-Q62):

NEW QUESTION # 57
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

Answer: A,C


NEW QUESTION # 58
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

Answer: B,C,D

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run


NEW QUESTION # 59
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?

Answer: C


NEW QUESTION # 60
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?

Answer: E


NEW QUESTION # 61
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

Answer: B


NEW QUESTION # 62
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