Google Certified Professional Data Engineer Exam: Professional-Data-Engineer Exam


"Google Certified Professional Data Engineer Exam", also known as Professional-Data-Engineer exam, is a Google Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 433 Q&As to your Professional-Data-Engineer Exam preparation. In the Professional-Data-Engineer exam resources, you will cover every field and category in Google Cloud Certified Certification helping to ready you for your successful Google Certification.

  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Total Questions: 433

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Having more competitive advantage means that you will have more opportunities and have a job that will satisfy you. This is why more and more people have long been eager for the certification of Professional-Data-Engineer. There is no doubt that obtaining this Professional-Data-Engineer certification is recognition of their ability so that they can find a better job and gain the social status that they want. Most people are worried that it is not easy to obtain the certification of Professional-Data-Engineer, so they dare not choose to start. We are willing to appease your troubles and comfort you. We are convinced that our Professional-Data-Engineer test material can help you solve your problems. Compared to other learning materials, our products are of higher quality and can give you access to the Professional-Data-Engineer certification that you have always dreamed of. Now let me introduce our Professional-Data-Engineer test questions for you. I will show you our study materials.

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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

  • Performing Spark on Cloud Dataproc
  • Prebuilt ML Models APIs for Unsaturated Data
  • Advanced BigQuery Performance and Functionality
  • Introduction to Data Engineering
  • Big Data Analytics with Cloud Al Platform Notebook
  • Introduction to Building Batch Data Pipelines
  • Serverless Messaging Using Cloud Sub/Pub
  • Creating a Data Lake
  • Custom Model building Utilizing Cloud AutoML
  • Building a Data Warehouse
  • Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
  • Bigtable Streaming Features and High-Throughput BigQuery
  • Serverless Data Processing with Cloud Dataflow
  • Introduction to Processing Streaming Data
  • Cloud Dataflow Streaming Features
  • Custom Model building Using SQL in BigQuery ML
  • Production ML Pipelines and use of Kubeflow

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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

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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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What is the duration, language, and format of Google Professional Data Engineer Exam

  • Format: Multiple choices, multiple answers
  • Passing score: 80%
  • Length of Examination: 120 minutes
  • Cost: $200
  • Language: English (U.S.), Japanese, Spanish, and Portuguese
  • Number of Questions: 50-60

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Deciding the degree of data normalization
  • 2. Designing the data model
  • 3. Defining architecture to support data access patterns
  • 4. Mapping business requirements
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Lifecycle management of data
  • 3. Analyzing data access patterns
Topic 2: Ingesting and processing the data (~20% of the exam)20%- 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
- Performing security considerations
  • 1. Data encryption
  • 2. Identity and Access Management (IAM)
  • 3. Auditing, privacy, and compliance
Topic 3: Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Batch processing
  • 2. Streaming (e.g., windowing, late arriving data)
  • 3. Data acquisition and import
  • 4. Processing logic
  • 5. Integrating with new data sources
  • 6. AI data enrichment
- 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. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 2. Cluster sizing and autoscaling
  • 3. Cost optimization
Topic 4: Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
Topic 5: Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Recovering from failures
  • 3. Planning for monitoring and alerting
- Automating data processes
  • 1. Workflow orchestration
  • 2. Scheduling jobs
  • 3. Continuous integration and continuous deployment (CI/CD)
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting

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