Prepare with Google : Professional-Data-Engineer日本語 exam braindumps as your best preparation materials

Updated: Sep 15, 2026

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Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

  • Hire BigQuery and Cloud SQL for interactive data analysis
  • Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
  • Create ML models using BigQuery ML, APIs, and AutoML.
  • Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud

Reference: https://cloud.google.com/certification/data-engineer

Prerequisites

There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.

Google Professional-Data-Engineer日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified Professional Data Engineer
Exam Number:Professional-Data-Engineer
Passing Score:Not officially published (estimated ~80%)
Certificate Validity Period:2 years
Exam Price:$200 USD
Real Exam Qty:50-60
Exam Format:Multiple-choice, Multiple-select
Available Languages:Japanese, English
Related Certifications:Google Cloud Certified Professional Data Engineer
Exam Duration:120 minutes
Sample Questions:Google Professional-Data-Engineer日本語 Sample Questions
Exam Way:Online (remote proctored) or at a testing center (Kryterion)
Pre Condition:No mandatory prerequisites. Recommended: 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 Professional-Data-Engineer日本語 Exam Syllabus Topics:

SectionWeightObjectives
Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Batch processing
  • 2. Processing logic
  • 3. Data acquisition and import
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. AI data enrichment
  • 6. Integrating with new data sources
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
- Designing data processing resources
  • 1. Cost optimization
  • 2. Cluster sizing and autoscaling
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
Maintaining and automating data workloads (~15% of the exam)15%- Automating data processes
  • 1. Workflow orchestration
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Scheduling jobs
- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Performing data quality and validation checks
  • 3. Planning for monitoring and alerting
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Defining architecture to support data access patterns
  • 3. Designing the data model
  • 4. Deciding the degree of data normalization
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
- Using a data lake
  • 1. Managing the lake (data discovery, access, cost controls)
  • 2. Monitoring the data lake
  • 3. Processing data
- Selecting storage systems
  • 1. Analyzing data access patterns
  • 2. Lifecycle management of data
  • 3. Planning for storage costs and performance
Ingesting and processing the data (~20% of the exam)20%- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
- Performing security considerations
  • 1. Auditing, privacy, and compliance
  • 2. Data encryption
  • 3. Identity and Access Management (IAM)

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