Google Professional Data Engineer PRACTICE EXAM

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Description

Practice Exam Overview

This practice exam is designed to comprehensively cover all the topics included in the certification exam. It is divided into 6 sections, with each section containing 60 questions. These sections will help you assess your knowledge across all relevant areas and ensure thorough preparation for the certification exam.

 

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Google Professional Data Engineer Course Description

The Google Professional Data Engineer certification is designed for individuals who want to demonstrate their expertise in designing, building, maintaining, and optimizing data processing systems on Google Cloud Platform (GCP). This certification validates the skills required to implement data engineering solutions, utilizing a wide range of GCP services to support the lifecycle of data systems, from data ingestion and storage to processing and analysis.

The course helps learners prepare for the Google Professional Data Engineer exam by covering key topics such as data architecture, data modeling, data analysis, and machine learning on GCP. It also emphasizes the use of BigQuery, Dataflow, Cloud Pub/Sub, and other GCP services to solve complex data engineering challenges.

Course Objectives

Upon completion of the Google Professional Data Engineer course, learners will be able to:

  1. Design and Build Data Pipelines:

    • Architect and design scalable and efficient data pipelines for both batch and stream processing.

    • Use services such as Dataflow, Cloud Dataproc, and Cloud Pub/Sub to ingest, transform, and load data.

  2. Work with Data Storage Systems:

    • Implement the appropriate data storage solutions, such as BigQuery, Cloud Bigtable, Cloud Spanner, and Cloud SQL.

    • Ensure proper data governance, security, and performance optimization in these storage systems.

  3. Data Modeling and Analysis:

    • Create efficient and scalable data models for storing and querying data.

    • Use BigQuery for data analysis and implement best practices for query optimization and cost management.

  4. Build and Deploy Machine Learning Models:

    • Utilize AI Platform to build and deploy machine learning models.

    • Prepare data for machine learning workflows and use BigQuery ML for creating predictive models directly within BigQuery.

  5. Implement Security, Compliance, and Data Governance:

    • Design and implement robust security policies for data access control, data encryption, and compliance (e.g., GDPR, HIPAA).

    • Use Identity and Access Management (IAM) for data resource access management.

  6. Optimize Data Processing Workflows:

    • Monitor, debug, and troubleshoot data workflows and pipelines.

    • Optimize performance and scalability of data systems, considering factors such as cost, latency, and throughput.

Key Topics Covered

  • Data Engineering Basics: Introduction to GCP services for data engineering.

  • BigQuery: Data storage, optimization, and querying for large-scale datasets.

  • Dataflow & Apache Beam: Design and manage stream and batch data pipelines.

  • Cloud Pub/Sub: Ingest real-time event data and distribute it for further processing.

  • Machine Learning: Data preparation, training, and deployment of models using AI Platform.

  • Data Security & Compliance: Implementing data encryption, privacy, and regulatory controls.

  • Cloud Storage: Choosing and managing storage options based on use cases and workloads.

Target Audience

  • Data Engineers: Professionals who are responsible for designing, implementing, and managing data infrastructure and pipelines.

  • Cloud Architects: Individuals who focus on architecting solutions on Google Cloud and need to integrate data solutions.

  • Developers: Developers who want to broaden their skillset and understand how to work with large-scale data on GCP.

  • Machine Learning Engineers: Those who are looking to leverage cloud infrastructure for building data pipelines and deploying machine learning models.

Course Prerequisites

While there are no strict prerequisites, the following knowledge will be beneficial:

  • Basic understanding of Google Cloud Platform services.

  • Experience with SQL and cloud-based data engineering tools.

  • Familiarity with programming languages such as Python or Java for scripting and data pipeline development.




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