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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

  • Data analysts
  • Developers responsible for managing big data transformation initiatives
  • Data scientists
  • Data architects
  • Data engineers
  • Business analysts

This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

  • Machine Learning
  • Scaling Data Analytics
  • Data Analytics on the Cloud
  • Introducing Google Cloud Platform ‘
  • Additional Resources
  • Compute and Storage Fundamentals
  • Data Processing Architectures

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

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How to Prepare For Google Professional Data Engineer Exam

Preparation Guide for Google Professional Data Engineer Exam

Introduction to Google Professional Data Engineer Exam

Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.

The Professional Data Engineer exam assesses your ability to:

  • Build and operationalize data processing systems
  • Ensure solution quality
  • Design data processing systems
  • Operationalize machine learning models

Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.

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:

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

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ingesting and processing the data (~20% of the exam)20%- Performing security considerations
  • 1. Identity and Access Management (IAM)
  • 2. Data encryption
  • 3. Auditing, privacy, and compliance
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
Topic 2: Designing data processing systems (~30% of the exam)30%- 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
- Designing data pipelines
  • 1. Streaming (e.g., windowing, late arriving data)
  • 2. Batch processing
  • 3. Data acquisition and import
  • 4. Integrating with new data sources
  • 5. Processing logic
  • 6. AI data enrichment
Topic 3: 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
Topic 4: Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Planning for monitoring and alerting
  • 2. Recovering from failures
  • 3. Performing data quality and validation checks
- Automating data processes
  • 1. Scheduling jobs
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Workflow orchestration
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
Topic 5: 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
- 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
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Processing data
  • 3. Managing the lake (data discovery, access, cost controls)
- 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

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