Google GCP-DE dumps - in .pdf

GCP-DE pdf
  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Jul 26, 2026
  • Q & A: 77 Questions and Answers
  • PDF Price: $59.99

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  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Version: V12.35   Updated: Jul 26, 2026
  • Q & A: 77 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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Google GCP-DE dumps - Testing Engine

GCP-DE Testing Engine
  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Version: V12.35   Updated: Jul 26, 2026
  • Q & A: 77 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Operationalizing data and ML pipelines30%- Pipeline automation and orchestration
  • 1. Scheduling and monitoring pipelines
    • 2. Cloud Composer workflows
      - Monitoring and troubleshooting
      • 1. Logging and observability
        • 2. Performance optimization and debugging
          Topic 2: Designing data processing systems20%- Storage and data modeling
          • 1. Data lake architecture on Google Cloud Storage
            • 2. Data warehouse design using BigQuery
              - Data pipeline architecture design
              • 1. Scalable data ingestion design
                • 2. Batch vs streaming data processing selection
                  Topic 3: Maintaining and optimizing data and ML solutions20%- Machine learning integration
                  • 1. BigQuery ML usage
                    • 2. Vertex AI integration for pipelines
                      - Security and governance
                      • 1. IAM and access control
                        • 2. Data encryption and compliance
                          Topic 4: Building and operationalizing data processing systems30%- Data ingestion and transformation
                          • 1. ETL/ELT workflows
                            • 2. Pub/Sub streaming ingestion
                              - Data pipeline implementation
                              • 1. Dataproc and Spark-based processing
                                • 2. Dataflow pipeline development

                                  Google Data Engineer Sample Questions:

                                  1. An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
                                  They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

                                  A) BigQuery
                                  B) Cloud SQL
                                  C) Cloud Datastore
                                  D) Cloud BigTable


                                  2. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

                                  A) Use the WHERE_PARTITIONTIME pseudo column
                                  B) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
                                  C) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
                                  D) Use the TABLE_DATE_RANGE function


                                  3. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

                                  A) VPN
                                  B) HTTPS
                                  C) HTTP
                                  D) SOCKS


                                  4. 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) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
                                  D) Assemble 1,000 files into a tape archive (TAR) fil
                                  E) Introduce data compression for each file to increase the rate file of file transfer.
                                  F) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.


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


                                  Solutions:

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

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