| Exam Code | Google-PDE |
| Questions and Answers | 241 |
| Premium Access | Yes |
| Online Test Engine | Yes |
| Comprehensive Q&A | Yes |
| Success Rate | 98% |
| Real Questions | Yes |
| Updated Regularly | Yes |
| Portable Files | ✔ |
| Unlimited Download | ✔ |
| 100% Secured | ✔ |
| Confidentiality | 100% |
| Success Guarantee | 100% |
| Any Hidden Cost | $0.00 |
| Auto Recharge | No |
| Updates Intimation | by Email |
| Technical Support | Free |
| PDF Compatibility | Windows, Android, iOS, Linux |
| Test Engine Compatibility | Mac/Windows/Android/iOS/Linux |
| Sample Questions |  |
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CERTIFICATION EXAM PREMIUM PDF
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A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
The Professional Data Engineer exam assesses your ability to:
- Design data processing systems
- Build and operationalize data processing systems
- Operationalize machine learning models
- Ensure solution quality
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
You can download a free PDF of the Google-PDE practice test and study guide to try before purchasing the premium files. To ace the exam, simply download the Google-PDE exam questions and answers file, memorize the content, and practice with the VCE Exam Simulator. This will ensure you are fully prepared for the real test.
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