• Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Certification Provider: Google
  • Corresponding Certification:Google Cloud Certified
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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.

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

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

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Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

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How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

  • Step3: Search for the exam name Google Professional Data Engineer
  • Step2: Sign in or sign up to your Google Cloud Webassessor account
  • Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
  • Step1: Visit the Google Cloud Webassessor Website

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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. Identity and Access Management (IAM)
  • 2. Auditing, privacy, and compliance
  • 3. Data encryption
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. Performing data quality and validation checks
  • 2. Planning for monitoring and alerting
  • 3. Recovering from failures
- Automating data processes
  • 1. Scheduling jobs
  • 2. Workflow orchestration
  • 3. Continuous integration and continuous deployment (CI/CD)
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
- Designing data pipelines
  • 1. Data acquisition and import
  • 2. AI data enrichment
  • 3. Batch processing
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. Integrating with new data sources
  • 6. Processing logic
- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Analyzing data access patterns
  • 3. Lifecycle management of data
- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Designing the data model
  • 3. Deciding the degree of data normalization
  • 4. Defining architecture to support data access patterns
- 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
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. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards

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