Professional Data Engineer: Professional Data Engineer on Google Cloud Platform certification video training course by prepaway along with practice test questions and answers, study guide and exam dumps provides the ultimate training package to help you pass.

GCP Professional Data Engineer Exam: Practice Test Series

Course Overview

This course is designed to prepare candidates for the Google Cloud Professional Data Engineer exam. It focuses on the knowledge and skills required to design, build, maintain, and optimize data processing systems in Google Cloud. The course emphasizes practical experience, real-world scenarios, and exam-focused strategies.

The curriculum combines theory, hands-on labs, and practice assessments. Candidates will learn how to manage data workflows, ensure data quality, and implement scalable solutions using GCP technologies. By the end of this course, learners will be confident in applying data engineering concepts in cloud environments.

Course Description

This training course covers all domains of the Professional Data Engineer exam. It introduces core GCP services, data pipelines, storage solutions, and analytics tools. Students will gain expertise in data modeling, database design, and machine learning integration.

The course emphasizes both technical skills and best practices. Learners will understand how to manage secure and efficient data systems while optimizing performance and costs. Practical exercises reinforce understanding and ensure learners are prepared for the exam and real-world data engineering tasks.

Who This Course is For

This course is ideal for data professionals who want to specialize in Google Cloud technologies. It is suitable for data engineers, data analysts, machine learning practitioners, and cloud architects. Individuals with experience in databases, analytics, or cloud computing will benefit most.

Beginners in GCP are welcome, but some familiarity with cloud concepts and SQL will help. The course also prepares professionals aiming for career growth, certification, and opportunities in cloud-based data engineering.

Course Requirements

Students should have a foundational understanding of data structures, relational databases, and analytics concepts. Basic programming skills in Python or SQL are recommended. Familiarity with cloud environments and web services will make learning faster and more effective.

The course assumes candidates can work with data pipelines, perform queries, and handle datasets. Experience with batch and streaming data processing is beneficial. Candidates should be prepared for hands-on labs using GCP services such as BigQuery, Dataflow, and Pub/Sub.

Course Modules

Introduction to Google Cloud Platform

This module covers the fundamentals of GCP. Learners will explore the architecture, core services, and global infrastructure. Understanding regions, zones, and projects is essential for designing scalable data solutions. Students will also learn about resource management and billing.

Data Storage and Database Services

Candidates will learn about different storage options in GCP, including Cloud Storage, Bigtable, Firestore, and Spanner. This module emphasizes choosing the right storage based on use cases, performance, and cost. Data modeling concepts and schema design are also covered in detail.

Data Processing and Pipelines

This module focuses on data ingestion, processing, and transformation. Students will explore batch and streaming data workflows using Dataflow, Dataproc, and Pub/Sub. Practical exercises will help learners build end-to-end pipelines and understand processing best practices.

Data Analysis and Visualization

Learners will gain skills in analyzing large datasets using BigQuery, Data Studio, and Looker. The module covers SQL-based querying, reporting, and building dashboards. Students will learn to interpret data efficiently and create actionable insights for business decisions.

Machine Learning and AI Integration

This module introduces integrating ML models into data pipelines. Candidates will explore AI Platform, AutoML, and TensorFlow on GCP. Students will learn how to preprocess data, train models, and deploy predictions within GCP workflows.

Security, Compliance, and Optimization

Data engineers must ensure secure and compliant solutions. This module covers Identity and Access Management, encryption, auditing, and network security. Students will also learn strategies to optimize performance and control costs across GCP services.

Exam Preparation Strategies

The final module of this part provides guidance for approaching the certification exam. Candidates will learn time management techniques, question analysis strategies, and common pitfalls to avoid. Practice exercises and scenario-based questions help reinforce learning.

Data Ingestion Strategies

Data ingestion is the first step in any data engineering workflow. This module covers methods to collect, import, and stream data into GCP. Candidates will explore batch ingestion using Cloud Storage and BigQuery Data Transfer Service. Batch ingestion is suitable for large, periodic datasets where latency is not critical.

Streaming ingestion is equally important for real-time analytics. Pub/Sub provides a reliable, scalable messaging service for event-driven architectures. Learners will understand how to design pipelines that handle high-throughput data streams efficiently. This module emphasizes designing ingestion workflows that are resilient, scalable, and cost-effective.

Data Transformation Techniques

Transforming raw data into usable formats is crucial. Dataflow and Dataproc enable scalable ETL (Extract, Transform, Load) operations. Candidates will learn about windowing, aggregation, and filtering in streaming data. Batch transformations using SQL, Dataflow templates, or Spark on Dataproc will also be explored.

The module focuses on designing pipelines that maintain data integrity and quality. Handling late-arriving data, schema evolution, and error management are critical skills for professional data engineers. Learners will practice implementing transformation logic to support analytics and ML pipelines.

Data Modeling and Schema Design

Data modeling ensures that data is structured efficiently for querying and storage. This module covers relational, NoSQL, and analytical data models. BigQuery schemas, partitioning, and clustering will be explained in detail. Candidates will learn when to use normalized, denormalized, or hybrid structures depending on performance requirements.

Schema evolution and data versioning are key topics. Learners will understand best practices for updating schemas without disrupting pipelines. This knowledge ensures that data systems remain flexible, maintainable, and performant.

Batch and Stream Processing

Handling data in both batch and streaming formats is essential for a professional data engineer. Batch processing involves processing large datasets periodically. Streaming involves continuous processing of incoming events. Candidates will explore the trade-offs, latency considerations, and design patterns for both approaches.

Dataflow provides unified support for batch and streaming. Learners will practice implementing real-time pipelines with windowing and triggering mechanisms. They will also learn monitoring techniques to detect bottlenecks and optimize throughput.

BigQuery Advanced Concepts

BigQuery is central to GCP data analytics. This module covers advanced topics such as partitioned and clustered tables, materialized views, and query optimization. Candidates will learn strategies to reduce query costs, improve performance, and manage large datasets efficiently.

Analytical functions, joins, and nested/repeated fields will be explained with examples. Students will practice writing optimized queries for real-world business scenarios. This module prepares learners to leverage BigQuery fully in professional data engineering tasks.

Data Quality and Governance

Ensuring data accuracy, consistency, and reliability is a critical responsibility. This module covers techniques for validating, cleansing, and monitoring data quality. Learners will explore Data Loss Prevention (DLP) for sensitive information and audit logging for compliance.

Data governance includes managing access controls, metadata, and lineage tracking. Candidates will understand how to implement policies that ensure regulatory compliance and operational efficiency. Proper governance ensures data is trustworthy and usable across the organization.

Machine Learning Pipelines

Integrating ML into data pipelines enables predictive analytics and automated decision-making. This module covers preprocessing, feature engineering, and model deployment in GCP. AI Platform and Vertex AI will be used to train, evaluate, and deploy ML models.

Learners will explore end-to-end ML workflows, including batch prediction and streaming inference. The module emphasizes designing pipelines that are reproducible, scalable, and maintainable. Candidates will also learn to monitor model performance over time.

Performance Tuning and Cost Optimization

Optimizing performance and cost is critical for enterprise data pipelines. This module teaches techniques to monitor, profile, and tune workflows. Candidates will explore resource allocation, query optimization, and autoscaling in Dataflow, BigQuery, and Dataproc.

Cost management strategies include storage tiering, query cost estimation, and data lifecycle management. Learners will practice identifying inefficiencies and implementing improvements. This knowledge ensures that GCP solutions are both high-performing and cost-effective.

Security Best Practices

Security is a foundational aspect of professional data engineering. This module covers Identity and Access Management (IAM), service accounts, encryption, and network security. Candidates will learn how to enforce least privilege access and protect sensitive data.

Security monitoring, logging, and auditing are emphasized. Learners will explore using Cloud Logging and Cloud Monitoring to detect anomalies. Implementing secure, compliant pipelines ensures that data engineering solutions meet enterprise standards.

Monitoring and Troubleshooting

Professional data engineers must monitor systems proactively. This module covers tools and techniques for observing pipeline health, identifying failures, and resolving issues. Candidates will learn alerting strategies, logging best practices, and debugging methods.

Students will practice troubleshooting common errors in Dataflow, BigQuery, and Pub/Sub pipelines. They will also learn how to handle job retries, backpressure, and service interruptions. Effective monitoring ensures reliability and smooth operation in production environments.

Cloud Architecture for Data Engineering

Designing robust cloud architectures is a critical skill for a professional data engineer. This module focuses on creating scalable, reliable, and maintainable systems in Google Cloud. Candidates will learn to structure projects, regions, and zones efficiently. Proper architecture ensures high availability, disaster recovery, and operational excellence.

Understanding multi-region deployments is essential. Learners will explore strategies for distributing data across zones to reduce latency and increase resilience. They will also examine patterns for integrating multiple GCP services into cohesive, end-to-end solutions.

Data Lake and Data Warehouse Concepts

Data lakes and data warehouses serve distinct purposes. This module examines the differences and use cases for each. A data lake stores raw, unstructured, or semi-structured data for flexible analysis. BigQuery acts as a modern data warehouse for structured, analytics-ready datasets.

Candidates will learn how to design hybrid architectures combining data lakes and warehouses. Strategies for ingesting, transforming, and querying data across these layers are covered. Practical exercises will demonstrate building pipelines that move data from raw ingestion to optimized analytics storage.

Real-Time Analytics

Real-time analytics is crucial for applications requiring immediate insights. This module explores streaming pipelines using Pub/Sub, Dataflow, and BigQuery streaming inserts. Learners will design pipelines that handle high-throughput, low-latency data.

Windowing, event-time processing, and late data handling are key concepts. Candidates will learn to implement real-time dashboards and alerts. Scenarios include monitoring IoT devices, tracking user activity, and detecting anomalies in streaming data.

Batch Data Analytics

Batch processing is essential for processing large volumes of data efficiently. Candidates will learn techniques for aggregating, summarizing, and transforming datasets in scheduled intervals. Dataflow and Dataproc provide scalable options for batch analytics.

This module emphasizes performance tuning, query optimization, and cost management. Learners will practice partitioning, clustering, and caching strategies in BigQuery to improve batch job efficiency. Case studies will demonstrate real-world applications of batch analytics in business reporting and forecasting.

Data Pipeline Design Patterns

Design patterns help data engineers create reliable, maintainable pipelines. This module introduces common patterns such as ETL, ELT, Lambda, and Kappa. Candidates will understand the benefits and trade-offs of each pattern.

Implementing idempotent pipelines ensures consistent results even when failures occur. Learners will practice designing pipelines that are modular, reusable, and easy to monitor. Best practices for logging, alerting, and error handling are included.

Data Security and Privacy

Data security is a non-negotiable aspect of professional data engineering. This module covers encryption at rest and in transit, key management, and secure service-to-service communication. Candidates will also explore anonymization, masking, and tokenization techniques to protect sensitive data.

Compliance with regulatory standards like GDPR and HIPAA is emphasized. Learners will practice implementing secure data pipelines and controlling access with IAM roles and policies. Monitoring and auditing capabilities are critical for maintaining secure operations.

Machine Learning Operations

Integrating ML into data pipelines requires operational knowledge. This module covers the lifecycle of ML workflows, including model training, evaluation, deployment, and monitoring. Vertex AI provides tools for building reproducible pipelines.

Candidates will explore batch prediction, streaming inference, and feature engineering pipelines. Best practices for versioning models, tracking experiments, and automating retraining are discussed. Practical exercises include deploying ML models in production-ready pipelines.

Monitoring, Logging, and Observability

Monitoring pipelines ensures data reliability and system health. This module covers Cloud Monitoring, Cloud Logging, and error reporting. Candidates will learn to create dashboards, set alerts, and track metrics across GCP services.

Observability techniques include tracing requests, identifying bottlenecks, and analyzing failure patterns. Learners will practice troubleshooting complex pipelines using logs, metrics, and debug tools. Effective monitoring reduces downtime and maintains pipeline efficiency.

Cost Management and Optimization

Cost efficiency is a critical skill for professional data engineers. This module explores strategies for managing storage, compute, and query costs. Learners will practice right-sizing resources, choosing the appropriate storage class, and optimizing queries in BigQuery.

Advanced techniques include partition pruning, table clustering, caching results, and scheduling jobs during off-peak hours. Candidates will understand cost trade-offs and how to implement scalable, efficient solutions without compromising performance.

Governance, Compliance, and Metadata Management

Data governance ensures that data is trustworthy, accessible, and compliant. This module covers metadata management, data lineage, and cataloging. Candidates will learn to use Data Catalog to organize, search, and manage datasets.

Compliance policies, auditing, and access controls are also covered. Learners will understand how to enforce governance across large-scale data systems while enabling analytics and machine learning workflows.

Advanced BigQuery Features

BigQuery provides advanced functionality for complex data analytics. This module explores materialized views, federated queries, scripting, and user-defined functions. Candidates will learn how to optimize queries and pipelines using these features.

Practical exercises include integrating external data sources, creating reusable transformations, and implementing advanced analytics logic. Candidates will gain hands-on experience in solving real-world analytics challenges.

Hands-On Labs Introduction

Practical experience is essential for mastering data engineering in GCP. This module introduces hands-on labs designed to simulate real-world scenarios. Candidates will practice building pipelines, querying datasets, and deploying machine learning models.

Labs emphasize best practices, troubleshooting, and optimization. Learners will gain confidence in using GCP services interactively, bridging the gap between theory and practical application. Each lab mirrors common tasks encountered in the certification exam.

Building Data Pipelines

Creating end-to-end pipelines is a core skill for data engineers. This module guides learners through designing, implementing, and testing pipelines using Dataflow and Dataproc. Candidates will practice ingesting data from Cloud Storage, Pub/Sub, and external sources.

Data transformation exercises include filtering, aggregation, and enrichment. Emphasis is placed on handling errors, late-arriving data, and retries. Learners will ensure pipelines are resilient, scalable, and maintainable.

BigQuery Labs

BigQuery is a central service for analytics and reporting. Candidates will practice loading, querying, and optimizing large datasets. Exercises include partitioned and clustered tables, materialized views, and federated queries.

Students will implement queries for business intelligence, reporting, and analytical tasks. Optimization labs cover query performance tuning, cost reduction techniques, and efficient data modeling.

Machine Learning Integration Labs

Machine learning pipelines are essential for predictive analytics. This module focuses on preprocessing, feature engineering, training, and deploying ML models using Vertex AI. Candidates will explore batch prediction, streaming inference, and automated retraining workflows.

Students will practice deploying models into production-ready pipelines, monitoring performance, and troubleshooting failures. Labs emphasize reproducibility, scalability, and integration with other GCP services.

Real-Time Streaming Labs

Real-time data processing is critical for modern applications. Learners will build pipelines using Pub/Sub and Dataflow to handle streaming data. Labs cover windowing, triggering, and event-time processing.

Candidates will implement dashboards, alerts, and real-time analytics use cases. Exercises simulate IoT telemetry, user activity tracking, and anomaly detection. The focus is on reliability, low-latency processing, and handling backpressure.

Security and Compliance Labs

Implementing secure pipelines is a fundamental responsibility. This module covers IAM roles, service accounts, encryption, and audit logging. Candidates will practice setting up secure data flows and controlling access at multiple levels.

Compliance labs simulate scenarios requiring GDPR, HIPAA, or internal data policies. Learners will enforce encryption, anonymization, and access controls while ensuring pipelines remain operational and efficient.

Monitoring and Troubleshooting Labs

Monitoring ensures pipeline reliability and performance. Candidates will use Cloud Monitoring, Logging, and Error Reporting to track system health. Labs include creating dashboards, setting alerts, and investigating failures.

Students will practice troubleshooting pipeline errors, job failures, and performance bottlenecks. They will learn best practices for logging, tracing, and resolving production issues efficiently.

Cost Optimization Labs

Cost management is critical in cloud data engineering. This module provides hands-on exercises for optimizing storage, compute, and query costs. Candidates will practice selecting storage classes, partitioning tables, and scheduling jobs strategically.

Labs cover query optimization in BigQuery, autoscaling in Dataflow, and efficient resource usage in Dataproc. Candidates will learn to balance performance and cost without compromising pipeline reliability.

Case Studies

Case studies provide context for real-world application of GCP services. Candidates will work on scenarios such as building a recommendation engine, real-time analytics for e-commerce, and IoT telemetry pipelines.

Each case study emphasizes architecture design, data modeling, pipeline implementation, and optimization. Learners will integrate multiple services, troubleshoot issues, and validate results, preparing them for complex exam scenarios.

Practice Exam Questions

Practice questions simulate the certification exam format. Candidates will answer scenario-based multiple-choice questions, design tasks, and problem-solving exercises.

Key focus areas include pipeline design, data ingestion, transformation, BigQuery optimization, ML integration, security, and cost management. Review and discussion follow each set of questions to reinforce learning.

Exam Strategies

Time management is critical for success. This module teaches strategies for reading questions carefully, prioritizing tasks, and managing exam duration. Candidates will learn how to approach tricky questions and case studies.

Familiarity with GCP console, CLI, and services will help navigate practical exam tasks. Candidates will practice interpreting scenarios, applying best practices, and choosing optimal solutions under exam conditions.

Review and Recap

This module consolidates key concepts from the previous parts. Learners will review architecture principles, data pipelines, ML integration, security, monitoring, and cost optimization.

Emphasis is placed on integrating theory, labs, and exam strategies. Candidates will ensure readiness for the final certification exam with confidence and practical knowledge.

Comprehensive Review of Data Ingestion

Data ingestion is the foundation of all data pipelines. Candidates will revisit batch ingestion techniques, including Cloud Storage, BigQuery Data Transfer Service, and Cloud SQL.

Streaming ingestion with Pub/Sub will also be reviewed. Learners will focus on designing pipelines capable of handling high throughput, low latency, and fault-tolerant data flow. This section emphasizes common pitfalls in ingestion and strategies to avoid data loss or duplication.

Advanced Data Transformation and Processing

Transforming data efficiently is a key skill for professional data engineers. Candidates will review batch processing using Dataflow and Dataproc, covering ETL and ELT workflows, windowing, aggregation, and filtering.

Streaming transformations are emphasized, including handling late-arriving data, watermarking, and event-time processing. Practical examples show how to implement transformations while maintaining data quality and system reliability.

Data Modeling Deep Dive

This section revisits data modeling techniques for relational, NoSQL, and analytical use cases. Candidates will practice creating optimized BigQuery schemas, partitioned and clustered tables, and denormalized structures for performance.

Schema evolution, versioning, and data validation techniques are discussed in depth. Learners will analyze case studies to determine the best modeling approach for different business scenarios, balancing performance, cost, and flexibility.

BigQuery Mastery

BigQuery is central to the GCP Data Engineer exam. This module covers advanced querying, optimization, and analytics techniques. Candidates will practice writing complex SQL queries, leveraging materialized views, and using federated queries to integrate external datasets.

Performance optimization strategies include caching, table clustering, partition pruning, and query cost estimation. Candidates will also explore scripting in BigQuery to automate repeated transformations and workflow tasks.

Machine Learning Pipelines Review

Integrating machine learning into data engineering workflows is increasingly important. Candidates will revisit preprocessing, feature engineering, model training, evaluation, and deployment in Vertex AI.

Batch predictions, streaming inference, and automated retraining workflows are reviewed. Practical exercises demonstrate how to incorporate ML models into production pipelines while ensuring reproducibility, scalability, and reliability.

Security and Compliance Consolidation

Security and compliance are fundamental responsibilities. Candidates will review IAM policies, encryption at rest and in transit, and service account management. Scenarios include implementing access controls, data masking, and anonymization techniques.

Regulatory compliance requirements such as GDPR, HIPAA, and internal data policies are reinforced. Learners will practice auditing pipelines and monitoring logs for compliance adherence while maintaining operational efficiency.

Monitoring, Observability, and Troubleshooting

Effective monitoring ensures pipeline reliability and performance. Candidates will review Cloud Monitoring, Logging, and Error Reporting practices. They will revisit creating dashboards, setting alerts, and interpreting metrics for proactive issue resolution.

Troubleshooting exercises include diagnosing job failures, pipeline bottlenecks, and data inconsistencies. Strategies for implementing idempotent and fault-tolerant pipelines are emphasized to prevent recurring issues.

Cost Optimization Strategies

Cost efficiency is a critical exam domain. Candidates will review storage optimization, query cost management, and compute resource allocation. Advanced strategies include selecting appropriate storage classes, partitioning data for query efficiency, and scheduling jobs to minimize costs.

Learners will analyze case studies demonstrating trade-offs between performance and cost. Techniques such as query optimization in BigQuery, autoscaling in Dataflow, and resource management in Dataproc are revisited.

Governance and Metadata Management

Data governance ensures that data is accessible, trusted, and compliant. Candidates will review metadata management, data lineage tracking, and cataloging using Data Catalog.

Scenario-based exercises focus on enforcing access controls, tracking dataset ownership, and maintaining governance across large-scale pipelines. Learners will integrate governance strategies into daily operations for operational excellence.

Advanced Hands-On Labs

Part 5 includes extensive hands-on labs simulating real-world challenges. Candidates will build full-scale pipelines, incorporating ingestion, transformation, storage, analytics, and machine learning components.

Labs cover batch and streaming data, multi-region deployments, high-volume datasets, and cost-effective architectures. Each lab reinforces best practices in security, monitoring, optimization, and governance. Candidates gain confidence in managing end-to-end solutions.

Real-World Case Studies

Case studies provide applied scenarios for learners. Examples include building recommendation engines for e-commerce, real-time analytics for IoT telemetry, predictive maintenance pipelines, and fraud detection workflows.

Candidates will integrate multiple GCP services, troubleshoot pipeline issues, optimize performance, and ensure compliance. These case studies simulate complex exam scenarios to prepare learners for practical questions.

Full-Length Practice Exams

Candidates will undertake full-length practice exams mimicking the actual certification test. Questions cover multiple-choice, scenario-based, and design problem formats.

Practice exams focus on all exam domains, including data pipelines, BigQuery, ML integration, security, monitoring, cost management, and governance. Immediate review of answers and explanations helps reinforce understanding and identify areas for improvement.

Exam Strategy and Time Management

Effective exam strategies are essential for success. Candidates will learn to read questions carefully, analyze scenarios, and identify the best solutions quickly. Time management techniques include pacing strategies, prioritizing high-weight questions, and avoiding common traps.

Candidates will also practice interpreting real-world scenarios under timed conditions. Strategic approaches to multiple-choice, case studies, and workflow design questions are emphasized to maximize scores.

Advanced Tips for Success

This module shares insights from experienced GCP professionals. Candidates will learn tips for remembering key concepts, mapping scenarios to GCP services, and avoiding pitfalls.

Attention is given to critical exam areas such as pipeline optimization, data modeling choices, real-time analytics design, and ML integration. Candidates will consolidate knowledge and confidence to perform well on the exam.

Post-Exam Preparation and Continuous Learning

After the exam, candidates are encouraged to continue exploring GCP. This includes learning emerging services, following best practices, and participating in cloud communities.

Continuous practice, experimentation, and project-based learning ensure long-term proficiency. Learners are prepared not just to pass the exam, but to excel in real-world data engineering roles.

Prepaway's Professional Data Engineer: Professional Data Engineer on Google Cloud Platform video training course for passing certification exams is the only solution which you need.