The GCP Data Engineer Certification Exam is an exam offered by Google Cloud Platform (GCP) to certify that an individual has the skills and knowledge to design, build, and manage data processing systems on GCP. This certification demonstrates expertise in data processing systems, data analysis, and data visualization on GCP. The exam covers a range of topics, including designing data processing systems, building and operationalizing data processing systems, integrating storage systems, and analyzing and visualizing data. It is intended for individuals with experience working with GCP and a background in data engineering or data analysis.
GCP Data Engineer Certification Exam Glossary
- Google Cloud Platform (GCP): A suite of cloud computing services offered by Google, including computing, storage, networking, data analytics, and machine learning.
- Data engineering: The process of designing, building, and maintaining the infrastructure and systems required to collect, store, process, and analyze large volumes of data.
- Data processing: The process of transforming and manipulating raw data into a usable format for analysis.
- Learn Data analysis: The process of examining and interpreting data using statistical and analytical methods to derive insights and make informed decisions.
- Data visualization: The representation of data in a graphical or visual format to make it easier to understand and interpret.
- Data pipeline: A set of processes and tools used to collect, transform, and load data from various sources into a data storage and analysis system.
- Big data: A term used to describe large, complex, and diverse datasets that are difficult to process using traditional data processing tools and techniques.
- ETL (Extract, Transform, Load): A process used to extract data from various sources, transform it into a usable format, and load it into a data storage and analysis system.
- Machine learning: A branch of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.
- Apache Hadoop: An open-source software framework used for distributed storage and processing of large datasets.
- Apache Spark: An open-source distributed computing system used for large-scale data processing and analysis.
- Cloud storage: A service that allows users to store and access data over the internet using cloud computing resources.
- Kubernetes: An open-source platform used for automating the deployment, scaling, and management of containerized applications.
GCP Data Engineer Certification Exam Guide
Here are some official resources provided by Google for the GCP Data Engineer Certification Exam:
- Exam Guide: Google Cloud Certified – Professional Data Engineer: This guide provides an overview of the exam, including the skills and knowledge tested, the exam format, and the exam policies. It also includes a list of recommended training resources and sample questions. You can find the guide here: https://cloud.google.com/certification/data-engineer
- Google Cloud Training: Google offers a variety of training courses for the GCP Data Engineer Certification Exam, including instructor-led courses, on-demand courses, and hands-on labs. You can find the training resources here: https://cloud.google.com/training/data-ml
- Practice Exam: Google offers a practice exam for the GCP Data Engineer Certification Exam. The practice exam includes sample questions that are similar to those on the actual exam and provides feedback on your performance. You can access the practice exam here: https://cloud.google.com/certification/practice-exam/data-engineer
- Official Documentation: Google provides extensive documentation for GCP services and technologies, which can be useful in preparing for the exam. You can find the documentation here: https://cloud.google.com/docs/
- Community Resources: The Google Cloud community provides a variety of resources, including forums, blogs, and meetups, where you can connect with other professionals and learn from their experiences. You can find the community resources here: https://cloud.google.com/community
GCP Data Engineer Certification Exam Tips and Tricks
Here are some tips and tricks that can help you prepare for and pass the GCP Data Engineer Certification Exam:
- Review the Exam Guide: The Exam Guide provides detailed information on the exam format, the topics covered, and the types of questions you can expect. Read through the guide carefully and make note of any areas where you need to focus your study efforts.
- Get Hands-On Experience: The GCP Data Engineer Certification Exam tests your ability to design and build data processing systems on GCP. Hands-on experience with GCP services and technologies is crucial for success on the exam. Work on real-world projects or complete GCP labs to gain practical experience.
- Use Official Training Resources: Google provides a variety of official training resources, including instructor-led courses, on-demand courses, and hands-on labs. These resources are designed to help you prepare for the exam and cover all the topics you need to know.
- Practice with Sample Questions: Google offers a practice exam that includes sample questions similar to those on the actual exam. Take the practice exam multiple times to familiarize yourself with the exam format and identify areas where you need more practice.
- Focus on Key Topics: The GCP Data Engineer Certification Exam covers a wide range of topics, but some areas are more important than others. Focus your study efforts on key topics such as data processing, data storage, and data analysis.
- Manage Your Time: The GCP Data Engineer Certification Exam is a timed exam, so it’s important to manage your time effectively. Make sure you understand the format of the exam and how much time you have for each section. Don’t spend too much time on any one question.
GCP Professional Data Engineer Exam Preparation Guide
We understand how challenging the Google Cloud Certified Professional Data Engineer Exam is. You must work on how unique your preparation is and what study resources/training you are using to minimize this. The best way to begin is with the exam topics. Exam objectives are organized into parts, which allows you to go over all of the topics in order. Furthermore, this will assist you in separating the weak and strong parts. Then, in accordance, begin revision. Furthermore, examining the test guide allows you to identify if your skills correspond to the exam’s topics
1. Getting familiar with the exam guide
The Google Cloud Certified Professional Data Engineer Exam guide includes a comprehensive list of subjects that may be covered on the exam, allowing you to decide whether your abilities match the exam’s objectives.
Section 1: Designing data processing systems (22%)
1.1 Designing for security and compliance. Considerations include:
- Identity and Access Management (e.g., Cloud IAM and organization policies) (Google Documentation: Identity and Access Management)
- Data security (encryption and key management) (Google Documentation: Default encryption at rest)
- Privacy (e.g., personally identifiable information, and Cloud Data Loss Prevention API) (Google Documentation: Sensitive Data Protection, Cloud Data Loss Prevention)
- Regional considerations (data sovereignty) for data access and storage (Google Documentation: Implement data residency and sovereignty requirements)
- Legal and regulatory compliance
1.2 Designing for reliability and fidelity. Considerations include:
- Preparing and cleaning data (e.g., Dataprep, Dataflow, and Cloud Data Fusion) (Google Documentation: Cloud Data Fusion overview)
- Monitoring and orchestration of data pipelines (Google Documentation: Orchestrating your data workloads in Google Cloud)
- Disaster recovery and fault tolerance (Google Documentation: What is a Disaster Recovery Plan?)
- Making decisions related to ACID (atomicity, consistency, isolation, and durability) compliance and availability
- Data validation
1.3 Designing for flexibility and portability. Considerations include
- Mapping current and future business requirements to the architecture
- Designing for data and application portability (e.g., multi-cloud and data residency requirements) (Google Documentation: Implement data residency and sovereignty requirements, Multicloud database management: Architectures, use cases, and best practices)
- Data staging, cataloging, and discovery (data governance) (Google Documentation: Data Catalog overview)
1.4 Designing data migrations. Considerations include:
- Analyzing current stakeholder needs, users, processes, and technologies and creating a plan to get to desired state
- Planning migration to Google Cloud (e.g., BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, Google Cloud networking, Datastream) (Google Documentation: Migrate to Google Cloud: Transfer your large datasets, Database Migration Service)
- Designing the migration validation strategy (Google Documentation: Migrate to Google Cloud: Best practices for validating a migration plan, About migration planning)
- Designing the project, dataset, and table architecture to ensure proper data governance (Google Documentation: Introduction to data governance in BigQuery, Create datasets)
Section 2: Ingesting and processing the data (25%)
2.1 Planning the data pipelines. Considerations include:
- Defining data sources and sinks (Google Documentation: Sources and sinks)
- Defining data transformation logic (Google Documentation: Introduction to data transformation)
- Networking fundamentals
- Data encryption (Google Documentation: Data encryption options)
2.2 Building the pipelines. Considerations include:
- Data cleansing
- Identifying the services (e.g., Dataflow, Apache Beam, Dataproc, Cloud Data Fusion, BigQuery, Pub/Sub, Apache Spark, Hadoop ecosystem, and Apache Kafka) (Google Documentation: Dataflow overview, Programming model for Apache Beam)
- Transformation:
- Batch (Google Documentation: Get started with Batch)
- Streaming (e.g., windowing, late arriving data)
- Language
- Ad hoc data ingestion (one-time or automated pipeline) (Google Documentation: Design Dataflow pipeline workflows)
- Data acquisition and import (Google Documentation: Exporting and Importing Entities)
- Integrating with new data sources (Google Documentation: Integrate your data sources with Data Catalog)
2.3 Deploying and operationalizing the pipelines. Considerations include:
- Job automation and orchestration (e.g., Cloud Composer and Workflows) (Google Documentation: Choose Workflows or Cloud Composer for service orchestration, Cloud Composer overview)
- CI/CD (Continuous Integration and Continuous Deployment)
Section 3: Storing the data (20%)
3.1 Selecting storage systems. Considerations include:
- Analyzing data access patterns (Google Documentation: Data analytics and pipelines overview)
- Choosing managed services (e.g., Bigtable, Cloud Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore) (Google Documentation: Google Cloud database options)
- Planning for storage costs and performance (Google Documentation: Optimize cost: Storage)
- Lifecycle management of data (Google Documentation: Options for controlling data lifecycles)
3.2 Planning for using a data warehouse. Considerations include:
- Designing the data model (Google Documentation: Data model)
- Deciding the degree of data normalization (Google Documentation: Normalization)
- Mapping business requirements
- Defining architecture to support data access patterns (Google Documentation: Data analytics design patterns)
3.3 Using a data lake. Considerations include
- Managing the lake (configuring data discovery, access, and cost controls) (Google Documentation: Manage a lake, Secure your lake)
- Processing data (Google Documentation: Data processing services)
- Monitoring the data lake (Google Documentation: What is a Data Lake?)
3.4 Designing for a data mesh. Considerations include:
- Building a data mesh based on requirements by using Google Cloud tools (e.g., Dataplex, Data Catalog, BigQuery, Cloud Storage) (Google Documentation: Build a data mesh, Build a modern, distributed Data Mesh with Google Cloud)
- Segmenting data for distributed team usage (Google Documentation: Network segmentation and connectivity for distributed applications in Cross-Cloud Network)
- Building a federated governance model for distributed data systems
Section 4: Preparing and using data for analysis (15%)
4.1 Preparing data for visualization. Considerations include:
- Connecting to tools
- Precalculating fields (Google Documentation: Introduction to materialized views)
- BigQuery materialized views (view logic) (Google Documentation: Create materialized views)
- Determining granularity of time data (Google Documentation: Filtering and aggregation: manipulating time series, Structure of Detailed data export)
- Troubleshooting poor performing queries (Google Documentation: Diagnose issues)
- Identity and Access Management (IAM) and Cloud Data Loss Prevention (Cloud DLP) (Google Documentation: IAM roles)
4.2 Sharing data. Considerations include:
- Defining rules to share data (Google Documentation: Secure data exchange with ingress and egress rules)
- Publishing datasets (Google Documentation: BigQuery public datasets)
- Publishing reports and visualizations
- Analytics Hub (Google Documentation: Introduction to Analytics Hub)
4.3 Exploring and analyzing data. Considerations include:
- Preparing data for feature engineering (training and serving machine learning models)
- Conducting data discovery (Google Documentation: Discover data)
Section 5: Maintaining and automating data workloads (18%)
5.1 Optimizing resources. Considerations include:
- Minimizing costs per required business need for data (Google Documentation: Migrate to Google Cloud: Minimize costs)
- Ensuring that enough resources are available for business-critical data processes (Google Documentation: Disaster recovery planning guide)
- Deciding between persistent or job-based data clusters (e.g., Dataproc) (Google Documentation: Dataproc overview)
5.2 Designing automation and repeatability. Considerations include:
- Creating directed acyclic graphs (DAGs) for Cloud Composer (Google Documentation: Write Airflow DAGs, Add and update DAGs)
- Scheduling jobs in a repeatable way (Google Documentation: Schedule and run a cron job)
5.3 Organizing workloads based on business requirements. Considerations include:
- Flex, on-demand, and flat rate slot pricing (index on flexibility or fixed capacity) (Google Documentation: Introduction to workload management, Introduction to legacy reservations)
- Interactive or batch query jobs (Google Documentation: Run a query)
5.4 Monitoring and troubleshooting processes. Considerations include:
- Observability of data processes (e.g., Cloud Monitoring, Cloud Logging, BigQuery admin panel) (Google Documentation: Observability in Google Cloud, Introduction to BigQuery monitoring)
- Monitoring planned usage
- Troubleshooting error messages, billing issues, and quotas (Google Documentation: Troubleshoot quota errors, Troubleshoot quota and limit errors)
- Manage workloads, such as jobs, queries, and compute capacity (reservations) (Google Documentation: Workload management using Reservations)
5.5 Maintaining awareness of failures and mitigating impact. Considerations include:
- Designing system for fault tolerance and managing restarts (Google Documentation: Designing resilient systems)
- Running jobs in multiple regions or zones (Google Documentation: Serve traffic from multiple regions, Regions and zones)
- Preparing for data corruption and missing data (Google Documentation: Verifying end-to-end data integrity)
- Data replication and failover (e.g., Cloud SQL, Redis clusters) (Google Documentation: High availability and replicas)
2. Understanding concepts using the training methods
There is nothing better than training programs with appropriate study resources when it comes to understanding the concepts for the exam. GCP uses a variety of training resources to give in-depth information and insights regarding the exam. This might be necessary in order to prepare for the Professional Data Engineer exam’s challenging topic. Let’s have a look at them.
• Data Engineer learning path:
Data Engineers provide systems that maximize flexibility and scalability while adhering to all security requirements. However, to get better clarity related to this role and passing the exam, Google offers various course and skills badges. This include:
Big Data & Machine Learning Fundamentals
This course gives an overview of Google Cloud capabilities as well as a more in-depth look at data processing capabilities. However, this course introduces learners to Google Cloud’s big data capabilities. You will gain an overview of Google Cloud and a thorough understanding of the data processing and machine learning capabilities through a combination of lectures, demos, and hands-on experiments.
Data Engineering on Google Cloud Platform
Learn the process of data processing systems, developing end-to-end data pipelines, and analyzing data. Moreover, understand about using Dataproc to lift and move Hadoop workloads, using Dataflow to process batch and streaming data, and using Data Fusion and Cloud Composer to manage data pipelines, among other things.
Serverless Data Processing with Dataflow
Learn how to turn your business logic into Dataflow-compatible data processing apps. Review foundations, pipeline development, and operations, as well as the most critical lessons for running a data application on Dataflow.
Create and Manage Cloud Resources
Learn how to:
- use Cloud Shell to create commands
- launch your first virtual machine
- execute apps on Kubernetes Engine or with load balancing.
Performing Foundational Data, ML, and AI Tasks in Google Cloud
Learn about using big data, machine learning, and artificial intelligence. Use Google Cloud products like BigQuery, Cloud Speech API, and AI Platform to get started.
Engineer Data with Google Cloud
This quest is made up of specialized labs that will put your Google Cloud data engineering expertise to the test, ranging from BigQuery to Dataprep to Cloud Composer and Tensorflow.
• Google Cloud Free Tier
This gives you access to free resources for learning about Google Cloud services. This is beneficial for beginners who are new to the platform and need to learn the fundamentals. If, on the other hand, you’re a long-time Google Cloud user who wants to try out new solutions, the Google Cloud Free Tier has you covered.
• Other Additional Training resources
When it comes to certification exams like Google Cloud Certified Professional Data Engineer, the more study materials you have, the better. Therefore, you should work on obtaining a more in-depth understanding of the Data Engineer in order to have a solid revision. There are, however, some resources that you may look into:
- Taking a webinar for valuable exam tips and tricks, and insights from Googlers and industry experts.
- Google Cloud documentation
- Google Cloud solutions
• Become a part of Google Learning & Certification Hub
You’ve come to the right place whether you’re just getting started with Google Cloud, preparing for the exam, or having a full set of Google Cloud Certifications. Share best practices for preparing for certification, get informed about upcoming events, and connect with others on the same road.
Learning Forums
- This is the area to talk about getting certified and upgrading your abilities in Cloud technologies. Ask questions, and use your professional knowledge to remove any doubts.
Cloud Learning Logs
- Create a Cloud Learning Log to keep track of your progress, connect with others who are working toward the same goal, and get feedback from peers and mentors.
3. Know when you are prepared for taking the exam
Assume you’re taking an exam and you’re asked a question about a certain topic. Then you were asked a question on some other topic. This may make one feel nervous throughout the exam. However, you’re probably ready for the exam if you’re well prepared to manage these circumstances. And, the best way to work on gaining this confidence is to start evaluating using the Professional Data Engineer practice tests.
Practice exams are the most effective and helpful method of determining your level of preparation. The Google Cloud Certified Professional Data Engineer Practice Exams will help you discover weak areas in your preparation and reduce your chances of making mistakes in the future. Practicing for the test after finishing a topic will reveal your weaknesses and reduce your chances of making mistakes on exam day. Start taking full-length practice examinations after learning a certain topic to ensure strong revision.
Final Words
The GCP Data Engineer Certification will take two hours and include 50 questions. So, if you’ve done all of the above-mentioned focused studying and gone through the practice examinations, the actual exam shouldn’t be too difficult. And, in order to improve your preparation, you must concentrate on all of the essential areas. Only those who put in a significant amount of work, on the other hand, will be successful. Give it your all and study your best to pass the exams.
