Google Professional Data Engineer- GCP EXAMS

Google Cloud certifications are among the highest paying IT certifications of 2022.

The Professional Data Engineer exam assesses your ability to:

1: Designing data processing systems

1.1 Selecting the appropriate storage technologies. Considerations include:

● Mapping storage systems to business requirements

● Data modeling

● Trade-offs 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., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, 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, Datastore, Memorystore)

● Storage costs and performance

● Life cycle 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 (AI Platform Prediction and Training, 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., Cloud Monitoring)

● 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., 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., multicloud, data residency requirements)

● Data staging, cataloging, and discovery