Google Google-PDE : Professional Data Engineer on Google Cloud Platform test Dumps

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Latest 2024 Updated Google Professional Data Engineer on Google Cloud Platform Syllabus
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Exam Number : Google-PDE
Exam Name : Professional Data Engineer on Google Cloud Platform
Vendor Name : Google
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Google-PDE test Format | Google-PDE Course Contents | Google-PDE Course Outline | Google-PDE test Syllabus | Google-PDE test Objectives

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 test 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

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