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Test Number : Google-PDE
Test Name : Professional Data Engineer on Google Cloud Platform
Vendor Name : Google
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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 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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Google Engineer learn

Google: be taught cloud abilities for free of charge with their new working towards tracks | Google-PDE test dumps and Cheatsheet

Google is offering a free direction for people who are on the hunt for skills to make use of containers, huge statistics and machine-studying models in Google Cloud.

The initial batch of classes includes 4 tracks aimed toward data analysts, cloud architects, information scientists and computer-gaining knowledge of engineers. 

The January 2021 route offers a quick track to have in mind key equipment for engineers and designers to use in Google Cloud. It contains a collection on getting all started in Google Cloud, another focussing on its BigQuery information warehouse, one which delves into the Kubernetes engine for managing containers, another for the Anthos software administration platform, and a remaining chapter on Google's ordinary interfaces for natural language processing and computing device imaginative and prescient AI. 

SEE: Nextcloud Hub: user guidance (free PDF) (TechRepublic)

individuals need to sign in to Google's "knowledge problem" and should receive 30 days' free access to Google Cloud labs. The free offer is aimed toward individuals who're new to Google Cloud and helps them get their head round terminology and ideas at the back of core infrastructure, equivalent to writing cloud shell commands, deploying virtual machines, and operating functions on Kubernetes. 

the first a part of the direction takes an estimated five hours to comprehensive and makes a speciality of creating and managing cloud elements. participants learn how to "write gcloud commands and use Cloud Shell, create and install digital machines in Compute Engine, run containerized purposes on Google Kubernetes Engine, and configure network and HTTP load balancers."

The courses are being run on Qwiklabs, a researching platform that Google Cloud bought in 2016 to teach americans and aid them get on board Google Cloud. or not it's supposed to tackle the shortage of employees who have cloud talents. 

contributors get a 30 minute tour of Qwiklabs; a lesson on how to create a Google Compute Engine virtual laptop or a windows Server example; an outline of how to use Google's Cloud Shell for having access to resource; and information on a way to set up community and net load balancers for an app operating in a VM.

The subsequent music explores cloud storage, entry control and id management, cloud monitoring, the use of the Google Cloud Platform Console, growing features using Google's command line, and how to make use of messages for purposes.

After finishing a challenge lab at the end of every track, participants can earn Google Cloud "ability badges". 

SEE: AWS is opening yet an extra cloud computing region

"each track will provide you an opportunity to earn different skill badges such because the Foundational Infrastructure skill badge or Foundational records, ML, and AI skill badge, which that you may share along with your network," explains Murriel Perez McCabe, a customer engineer at Google Cloud. 

To earn a skill badge, students should comprehensive a collection of hands-on labs on Google Cloud labs to be taught new cloud expertise after which ascertain their newfound talents by means of a remaining challenge lab assessment.


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