Google Professional Data Engineer (GCP) Practice Exam
The Google Professional Data Engineer (GCP) certification validates your ability to design, develop, and maintain data processing solutions on Google Cloud Platform (GCP). It assesses your proficiency in various aspects of data engineering, including data ingestion, transformation, storage, analysis, and visualization.
Who should pursue the Google Professional Data Engineer (GCP) Certification?
This certification is ideal for:
- Data engineers: seeking to validate their expertise in using GCP for data engineering tasks.
- Data architects: wanting to demonstrate their capabilities in designing data solutions on GCP.
- Software engineers: transitioning into data engineering roles and seeking to gain GCP-specific skills.
- Anyone seeking to:
- Advance their careers in data engineering or related fields.
- Showcase their proficiency in utilizing GCP for big data processing and analytics.
- Increase their marketability and earning potential in the data-driven job market.
Key Skills and Knowledge Assessed:
The Google Professional Data Engineer (GCP) exam focuses on various areas related to data engineering on GCP, including:
- Designing data pipelines: Understanding different data pipeline architectures and designing them effectively on GCP.
- Data ingestion: Utilizing various GCP services like Cloud Storage, Pub/Sub, and Cloud Dataflow to ingest data from diverse sources.
- Data transformation: Cleaning, transforming, and preparing data for analysis using tools like BigQuery and Cloud Dataproc.
- Data storage: Selecting and managing appropriate storage solutions on GCP, including BigQuery, Cloud SQL, and Cloud Storage.
- Data analysis and visualization: Analyzing data using tools like BigQuery and Bigtable, and creating visualizations using tools like Data Studio.
- Machine learning: Understanding the fundamentals of machine learning and its integration with data pipelines on GCP.
- Security and best practices: Implementing security best practices and managing access controls for data and resources on GCP.
Exam Details:
- Exam Provider: Google Cloud
- Format: case studies
- Number of Questions: 15
- Duration: 120 minutes (2 hours)
- Passing Score: Minimum score not publicly disclosed by Google (generally around 70%)
- Delivery: Testing center or online proctored
Course Outline
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