Try and practice the latest Google : Professional-Data-Engineer日本語 real questions & answers

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Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

  • Operationalizing machine learning models
  • Building data processing systems
  • Providing solution quality
  • Designing data processing systems

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

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.

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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

SectionWeightObjectives
Maintaining and automating data workloads (~15% of the exam)15%- Automating data processes
  • 1. Scheduling jobs
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Workflow orchestration
- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Recovering from failures
  • 3. Planning for monitoring and alerting
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
Ingesting and processing the data (~20% of the exam)20%- 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)
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Designing the data model
  • 3. Mapping business requirements
  • 4. Deciding the degree of data normalization
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
- 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
- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Analyzing data access patterns
  • 3. Planning for storage costs and performance
Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Streaming (e.g., windowing, late arriving data)
  • 2. Batch processing
  • 3. Data acquisition and import
  • 4. Processing logic
  • 5. AI data enrichment
  • 6. Integrating with new data sources
- 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. 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

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