Google Professional-Data-Engineer日本語 Q&A - in .pdf

  • Professional-Data-Engineer日本語 pdf
  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Aug 03, 2026
  • Q & A: 433 Questions and Answers
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  • Exam Code: Professional-Data-Engineer日本語
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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:

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

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

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

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Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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