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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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

This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

  • Cloud Dataflow Streaming Features
  • Introduction to Data Engineering
  • Serverless Data Processing with Cloud Dataflow
  • Serverless Messaging Using Cloud Sub/Pub
  • Introduction to Processing Streaming Data
  • Big Data Analytics with Cloud Al Platform Notebook
  • Custom Model building Using SQL in BigQuery ML
  • Creating a Data Lake
  • Custom Model building Utilizing Cloud AutoML
  • Prebuilt ML Models APIs for Unsaturated Data
  • Introduction to Building Batch Data Pipelines
  • Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
  • Building a Data Warehouse
  • Advanced BigQuery Performance and Functionality
  • Production ML Pipelines and use of Kubeflow
  • Performing Spark on Cloud Dataproc
  • Bigtable Streaming Features and High-Throughput BigQuery

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

Professional-Data-Engineer learning test was a high quality product revised by hundreds of experts according to the changes in the syllabus and the latest developments in theory and practice, based on historical questions and industry trends. Whether you are a student or an office worker, whether you are a rookie or an experienced veteran with years of experience, Professional-Data-Engineer guide torrent: Google Certified Professional Data Engineer Exam will be your best choice. The main advantages of our study materials include:

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Analyzing data access patterns
  • 3. Planning for storage costs and performance
- Using a data lake
  • 1. Processing data
  • 2. Monitoring the data lake
  • 3. Managing the lake (data discovery, access, cost controls)
- 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. Designing the data model
  • 3. Mapping business requirements
  • 4. Deciding the degree of data normalization
Designing data processing systems (~30% of the exam)30%- 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)
- Designing data pipelines
  • 1. Streaming (e.g., windowing, late arriving data)
  • 2. Integrating with new data sources
  • 3. Processing logic
  • 4. AI data enrichment
  • 5. Batch processing
  • 6. Data acquisition and import
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
- Automating data processes
  • 1. Continuous integration and continuous deployment (CI/CD)
  • 2. Scheduling jobs
  • 3. Workflow orchestration
- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Planning for monitoring and alerting
  • 3. Performing data quality and validation checks
Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
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)
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Performing security considerations
  • 1. Data encryption
  • 2. Identity and Access Management (IAM)
  • 3. Auditing, privacy, and compliance

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