Google GCP-DE dumps - in .pdf

GCP-DE pdf
  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Sep 30, 2026
  • Q & A: 77 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Sep 30, 2026
  • Q & A: 77 Questions and Answers
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  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Sep 30, 2026
  • Q & A: 77 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Operationalizing data and ML pipelines30%- Pipeline automation and orchestration
  • 1. Cloud Composer workflows
    • 2. Scheduling and monitoring pipelines
      - Monitoring and troubleshooting
      • 1. Performance optimization and debugging
        • 2. Logging and observability
          Building and operationalizing data processing systems30%- Data pipeline implementation
          • 1. Dataproc and Spark-based processing
            • 2. Dataflow pipeline development
              - Data ingestion and transformation
              • 1. ETL/ELT workflows
                • 2. Pub/Sub streaming ingestion
                  Designing data processing systems20%- Storage and data modeling
                  • 1. Data warehouse design using BigQuery
                    • 2. Data lake architecture on Google Cloud Storage
                      - Data pipeline architecture design
                      • 1. Batch vs streaming data processing selection
                        • 2. Scalable data ingestion design
                          Maintaining and optimizing data and ML solutions20%- Security and governance
                          • 1. Data encryption and compliance
                            • 2. IAM and access control
                              - Machine learning integration
                              • 1. Vertex AI integration for pipelines
                                • 2. BigQuery ML usage

                                  Google Data Engineer Sample Questions:

                                  Question #1

                                  Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
                                  You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

                                  • A. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
                                  • B. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
                                  • C. Assemble 1,000 files into a tape archive (TAR) fil
                                  • D. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
                                  • E. Introduce data compression for each file to increase the rate file of file transfer.
                                  • F. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D,F  🗳️

                                  Question #2

                                  Which of the following statements is NOT true regarding Bigtable access roles?

                                  • A. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
                                  • B. To give a user access to only one table in a project, you must configure access through your application.
                                  • C. You can configure access control only at the project level.
                                  • D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

                                  Explanation: Only visible for ValidTorrent members. You can sign-up / login (it's free).

                                  Question #3

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

                                  • A. Google Cloud Storage
                                  • B. File upload
                                  • C. Google Drive
                                  • D. Google Cloud SQL
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

                                  Explanation: Only visible for ValidTorrent members. You can sign-up / login (it's free).

                                  Question #4

                                  You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
                                  What should you do?

                                  • A. Create a dummy user and grant dataset access to that use
                                  • B. Use the service account's private key to access the dataset
                                  • C. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
                                  • D. Create groups for your users and give those groups access to the dataset
                                  • E. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
                                  • F. Create a service account and grant dataset access to that accoun
                                  Reveal Solution  Discussion  0

                                  Correct Answer: F  🗳️

                                  Question #5

                                  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?

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

                                  Correct Answer: A  🗳️

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