When it comes to preparing for any certification exam, there are no better learning resources other than Online Courses.
The demand for Google Cloud Platform certifications is largely due to the higher salary estimates for GCP-certified professionals. So, if you desire to become a GCP Data Engineer, then the new Testpreptraining course is ideal for you. Our experts have curated this online course, keeping in mind the need for the GCP Data Engineer Certification Exam. The following discussion will point out the quintessential aspects of the GCP Data Engineer certificate exam.
In addition, we’ll also be discussing all the highlights of our GCP Data Engineer Online Course. The central purpose of the following article focuses on assisting you to understand the prospects with the job role of a GCP Data Engineer. Not to mention, you’ll be able to observe the ways in which Testpreptraining GCP Data Engineer Online Course will be very helpful. So, without a further due, let’s begin.
GCP Data Engineer Certification Exam Overview
The Testpreptraining GCP Data Engineer training course is suitable for candidates with interest in data investigation. Candidates for the GCP Data Engineer certification exam assume roles for data-based decision making. The objective of the certification is the validation of the abilities of an individual for the collection, transformation, and publishing of data.
Prerequisites
The prerequisites for the GCP Data Engineer certification exam is vital for every aspiring Data Engineer. And, the most prominent highlight for the GCP Data Engineer is that it doesn’t require any prerequisites. However, candidates need to fulfil the recommended experience required for the GCP Data Engineer certification exam.
So, one needs a minimum of three years of industry experience in data-based roles along with more than one year of practical experience in the design and management of solutions using GCP can be helpful. Also, another crucial prerequisite that candidates must fulfil for authenticating their eligibility is candidate’s age. Candidates should be at least 18 years of age or more to appear for the examination.
GCP Data Engineer Exam Details
When it comes to preparing for any examination, basic exam details must be acknowledged by the candidate. Therefore, the Testpreptraining GCP Data Engineer online course outlines out the comprehensive course that can help qualify the examination. This course follows the same format of the actual certification exam as a reference. Knowledge about the basic information for the GCP professional data engineer certification exam can help in improving your confidence.
The Testpreptraining GCP Data Engineer training course aims at helping candidates find the right path for success.
Basic Exam Details
The GCP Data Engineer certification exam comprises of multiple-choice and multiple-select format for the questions. The total duration of the exam is 2 hours, and candidates can choose the test centre while scheduling the exam.
The registration fee for the exam is USD 200, along with applicable taxes. The GCP Data Engineer certification exam is available in only four languages i.e., English, Portuguese, Japanese, and Spanish. So, make sure you are familiar with at least one of the language.
For a better understanding of the examination, it’s important for you to understand all the exam objectives. Therefore, it’s important to get acquainted with the course outline.
Course Outline
The next important highlight of the Testpreptraining GCP Data Engineer online course is its alignment with exam objectives. The GCP Data Engineer certification exam verifies the candidate’s abilities for designing, building, operationalizing, securing, and monitoring data processing systems. So, the exam tests the following abilities of candidates.
- Design data processing systems
- Ingest and process the data
- Store the data
- Prepare and use data for analysis
- Maintain and automate data workloads
For the above mentioned, you can depend on the Testpreptraining GCP Data Engineer online course. Not only will this be helpful for covering all underlying topics in these exam domains but also help candidates have a better understanding of the exam. In the same vein, let us take a closer look at the subtopics in each exam domain.
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)
The Testpreptraining online course for GCP Data Engineer certification exam could thus ensure detailed coverage of the exam’s course outline.
The all NEW Testpreptraining GCP Data Engineer Online Course 
The Testpreptraining GCP Data Engineer video course is the best learning resource for aspiring GCP Data Engineers.
Course Overview
This particular online course will act as a bridge to qualify your GCP Data Engineer certification exam. So, below we are listing different features of our GCP Data Engineer certification online training course.
- Over 20 hours of comprehensive training videos.
- Covers all domains of the course outline, to get past the GCP Data Engineer exam.
- Unlimited access to the course.
- Flexibility for accessing the course on various devices.
Now, the most valuable highlight of the Testpreptraining GCP Data Engineer online course is its clarity. The course adopts a flexible approach in which you can find seventeen different modules. Seventeen may seem too long. But, when you understand that the course builds your foundation with every step and that each plays an important role.
Moreover, all the training videos for each subtopic in different course module covers all the necessary information required for you to qualify the examination. As a result, this online course will only prove to be in your favour. By devoting only a few hours every day, the result can astonish you.
Table of Content
So, below we are providing you with a list of all the modules presented in the Testpreptraining Online course.
- Compute Choices
- Storage
- Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
- Datastore ~ Document Database
- BigTable ~ HBase = Columnar Store
- BigQuery ~ Hive ~ OLAP
- Dataflow ~ Apache Beam
- Dataproc ~ Managed Hadoop
- Pub/Sub for Streaming
- Datalab ~ Jupyter
- TensorFlow and Machine Learning
- Regression in TensorFlow
- Vision, Translate, NLP and Speech: Trained ML APIs
- Networking
- Ops and Security
- Appendix: Hadoop Ecosystem
Benefits of Testpreptraining GCP Professional Data Engineer Online Course
Candidates should know that the online training course for GCP Data Engineer certification by Testpreptraining is a quality product. Various subject matter experts and certified professionals have designed the course with their years of experience and skills. Here are the noticeable ways in which the Testpreptraining GCP professional data engineer certification online course can help you.
- Candidates get an assured approach to gear up for the certification exam. How? The Testpreptraining online course aligns perfectly with the exam objectives and focuses only on the important ones. So, you only study for exactly what would form the basis of questions in the actual exam!
- The money-back-guarantee topped with an appealing price makes the Testpreptraining course more reasonable. Candidates could verify that their investment would find the right place at Testpreptraining.
- Testpreptraining also provides the assurance of promising customer support. The availability of customer support at Testpreptraining round the clock could also provide essential support to candidates. You don’t have to worry about your doubts about online training courses anymore.
- Comprehensive coverage of all exam topics helps you in preparing thoroughly for the exam. In addition, candidates also have the option of practice tests to improve their preparations.
Last words
Lastly, we would like to remind you, it’s time to take that plunge. Therefore, make sure, to begin with, the right pathway. In the same light, this online course would be your stepping stone. Not will it build a solid foundation but will also polish your skills, talents and achievements. Since showcasing your potential to the employers will enhance your resume. So, make sure to get certified with an excellent remark. As it’s not just a credential on the parer, but the knowledge you possess.
Make your resume stand out and become a Certified GCP Data Engineer. Try free practice tests here!
