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Google Professional Data Engineer (GCP) Practice Exam

Google Cloud Certified - Professional Data Engineer


About Google Cloud Certified - Professional Data Engineer Exam

The Professional Data Engineer exam enables data-driven decision making by collecting, transforming, and visualizing data. The sole objective of a Google Cloud Certified - Professional Data Engineer is to design, build, maintain, and troubleshoot data processing systems with a particular emphasis on the security, reliability, fault-tolerance, scalability, fidelity, and efficiency of the systems.


Exam Pattern for Google Cloud Certified - Professional Data Engineer

  • Language: English, Japanese, Spanish, and Portuguese.
  • Length: 2 hours
  • Registration fee: USD $200
  • Types of Questions: Multiple Choice and Multiple Select

* Note the exam has no prerequisites and must be taken in-person at one of our testing center locations.


Course Structure for Google Cloud Certified - Professional Data Engineer

Certified Professional Data Engineer analyzes data to gain insight into business outcomes, builds statistical models to support decision-making, and creates machine learning models to automate and simplify key business processes. The Google Cloud Certified - Professional Data Engineer exam assesses a candidates ability to -


1. Designing data processing systems

1.1 Selecting the appropriate storage technologies. Considerations include:

  • Mapping storage systems to business requirements
  • Data modeling
  • Tradeoffs involving latency, throughput, transactions
  • Distributed systems
  • Schema design


1.2 Designing data pipelines. Considerations include:

  • Data publishing and visualization (e.g., BigQuery)
  • Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
  • Online (interactive) vs. batch predictions
  • Job automation and orchestration (e.g., Cloud Composer)


1.3 Designing a data processing solution. Considerations include:

  • Choice of infrastructure
  • System availability and fault tolerance
  • Use of distributed systems
  • Capacity planning
  • Hybrid cloud and edge computing
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • At least once, in-order, and exactly once, etc., event processing


1.4 Migrating data warehousing and data processing. Considerations include:

  • Awareness of current state and how to migrate a design to a future state
  • Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
  • Validating a migration

2. Building and operationalizing data processing systems

2.1 Building and operationalizing storage systems. Considerations include:

  • Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
  • Storage costs and performance
  • Lifecycle management of data


2.2 Building and operationalizing pipelines. Considerations include:

  • Data cleansing
  • Batch and streaming
  • Transformation
  • Data acquisition and import
  • Integrating with new data sources


2.3 Building and operationalizing processing infrastructure. Considerations include:

  • Provisioning resources
  • Monitoring pipelines
  • Adjusting pipelines
  • Testing and quality control


3. Operationalizing machine learning models

3.1 Leveraging pre-built ML models as a service. Considerations include:

  • ML APIs (e.g., Vision API, Speech API)
  • Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
  • Conversational experiences (e.g., Dialogflow)


3.2 Deploying an ML pipeline. Considerations include:

  • Ingesting appropriate data
  • Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
  • Continuous evaluation


3.3 Choosing the appropriate training and serving infrastructure. Considerations include:

  • Distributed vs. single machine
  • Use of edge compute
  • Hardware accelerators (e.g., GPU, TPU)


3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include:

  • Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
  • Impact of dependencies of machine learning models
  • Common sources of error (e.g., assumptions about data)


4. Ensuring solution quality

4.1 Designing for security and compliance. Considerations include:

  • Identity and access management (e.g., Cloud IAM)
  • Data security (encryption, key management)
  • Ensuring privacy (e.g., Data Loss Prevention API)
  • Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))


4.2 Ensuring scalability and efficiency. Considerations include:

  • Building and running test suites
  • Pipeline monitoring (e.g., Stackdriver)
  • Assessing, troubleshooting, and improving data representations and data processing infrastructure
  • Resizing and autoscaling resources


4.3 Ensuring reliability and fidelity. Considerations include:

  • Performing data preparation and quality control (e.g., Cloud Dataprep)
  • Verification and monitoring
  • Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
  • Choosing between ACID, idempotent, eventually consistent requirements


4.4 Ensuring flexibility and portability. Considerations include:

  • Mapping to current and future business requirements
  • Designing for data and application portability (e.g., multi-cloud, data residency requirements)
  • Data staging, cataloging, and discovery


FAQs on Cloud Certified - Professional Data Engineer

1. What number of questions will there be in the exam?

As we update the certification exam over time to keep the current technology resultant the number of questions in an exam is subject to change. 40-60 questions are there in the most Microsoft Certification exams; although, the number can vary.


2. Is there any requirement to take an exam in English?

Microsoft Certification exams are offered in a variety of languages. Although, candidates can make a request for accommodation for an additional time those who must take the exam in English rather than in their native language. On a case-by-case basis approval for extra time is provided. Request test accommodations from Pearson VUE or Certiport.


3. What variety of questions appears on Microsoft Certification exams?

This level of information cannot be provided for each exam as Microsoft is constantly developing and pilot testing new question types. Although, you can review some possible exam formats and question types.


4. Is preparation for the performance-based exams are differently done from the other exams?

No. Regardless of the format of the question the skills measured remain the same. However, in the "Skills measured" section of the exam details page the knowledge and skills assessed in the exam are listed.


5. What does the score report look like?

A numeric score for overall exam performance is provided in the score report, status of pass/fail, and a bar chart showing performance in each skill area assessed in the exam. Using this information, the areas of strength and weakness of the candidates can be determined.


6. How the exam scores are calculated?

After the completion of your exam, the points you earned on each question are totalled and then compared with the cut score to determine whether the result is pass or fail.


7. Does Testprep Training offer Money Back Guarantee for the Exam Simulator?

Yes, we offer a 100% unconditional money back guarantee. In case you are not able to clear the exam for then, you can request for the full refund. Please note that we only refund the cost of product purchased from Testprep Training and not from the Microsoft Learning.


8. Is there any assistance from Testprep Training in terms of exam preparation?

Yes, Testprep Training offers email support for any certification related query while you are preparing for the exam using our practice exams. Your query will be handled by experts in due course.


9. Can we try the free test before purchasing the practice exam?

Yes, testprep training offers free practice tests for Cloud Certified - Professional Data Engineer which can be used before the final purchase for the complete test.


10. Do you provide any preparation guidance for this certification exam?

Yes, our experts frequently blog about the tips and tricks for exam preparation.


11. Do you offer any discount on the bulk purchase?

Yes, we offer nearly 50% discount for the order more than 10 products at a time. You can reach the testprep training Helpdesk for more details. The member of the support staff will respond as soon as possible.


12. For how long is the license valid after purchase?

Once purchased, the practice exams can be accessed for the lifetime.


13. Am I required to retake the exam? As the exams become updated with performance-based items.

No. The skills that are tested do not change; therefore, retesting is not necessary.


14. Do the exams with performance-based questions take longer to complete?

Yes. These exams may take longer to complete than exams that do not contain performance-based items. As performance-based questions are added to exams, you may see changes in the standardized exam times. No exam, however, will exceed 200 minutes, and the maximum seat time is 240 minutes.


15. What worth do the short answer questions have?

Most of the short answer questions are worth one point. In some cases, they might be worth more than one point. In these cases, we indicate within the question itself the number of points that it is worth.


For more FAQs

https://cloud.google.com/certification/faqs/#0


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  • In-depth and exhaustive explanation for every question
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  • Practice exam questions have been created on the basis of content outlined in the official documentation.
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