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

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Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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

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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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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%- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Recovering from failures
  • 3. Planning for monitoring and alerting
- Automating data processes
  • 1. Scheduling jobs
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Workflow orchestration
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
Topic 2: Storing the data (~20% of the exam)20%- Using a data lake
  • 1. Processing data
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Monitoring the data lake
- Selecting storage systems
  • 1. Analyzing data access patterns
  • 2. Planning for storage costs and performance
  • 3. Lifecycle management of data
- Designing for a data platform
  • 1. Building a federated governance model for distributed data systems
  • 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Mapping business requirements
  • 3. Deciding the degree of data normalization
  • 4. Designing the data model
Topic 3: 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
Topic 4: Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Cost optimization
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cluster sizing and autoscaling
- 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 pipelines
  • 1. Data acquisition and import
  • 2. Processing logic
  • 3. Streaming (e.g., windowing, late arriving data)
  • 4. Integrating with new data sources
  • 5. AI data enrichment
  • 6. Batch processing
Topic 5: Ingesting and processing the data (~20% of the exam)20%- Performing security considerations
  • 1. Auditing, privacy, and compliance
  • 2. Identity and Access Management (IAM)
  • 3. Data encryption
- 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. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines

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