Google Professional Data Engineer (GCP) Practice Exam

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: Multiple-choice and multiple select questions
• Number of Questions: 50-60
• 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 (22%)

1.1 Designing for security and compliance. Considerations include:
● Identity and Access Management (e.g., Cloud IAM and organization policies)
● Data security (encryption and key management)
● Privacy (e.g., personally identifiable information, and Cloud Data Loss Prevention API)
● Regional considerations (data sovereignty) for data access and storage
● Legal and regulatory compliance

1.2 Designing for reliability and fidelity. Considerations include:
● Preparing and cleaning data (e.g., Dataprep, Dataflow, and Cloud Data Fusion)
● Monitoring and orchestration of data pipelines
● Disaster recovery and fault tolerance
● Making decisions related to ACID (atomicity, consistency, isolation, and durability) compliance and availability
● Data validation

1.3 Designing for flexibility and portability. Considerations include:
● Mapping current and future business requirements to the architecture
● Designing for data and application portability (e.g., multi-cloud and data residency requirements)
● Data staging, cataloging, and discovery (data governance)

1.4 Designing data migrations. Considerations include:
● Analyzing current stakeholder needs, users, processes, and technologies and creating
a plan to get to desired state
● Planning migration to Google Cloud (e.g., BigQuery Data Transfer Service, Database
Migration Service, Transfer Appliance, Google Cloud networking, Datastream)
● Designing the migration validation strategy
● Designing the project, dataset, and table architecture to ensure proper data
governance

2. Ingesting and processing the data (25%)

2.1 Planning the data pipelines. Considerations include:
● Defining data sources and sinks
● Defining data transformation logic
● Networking fundamentals
● Data encryption

2.2 Building the pipelines. Considerations include:
● Data cleansing
● Identifying the services (e.g., Dataflow, Apache Beam, Dataproc, Cloud Data Fusion,
BigQuery, Pub/Sub, Apache Spark, Hadoop ecosystem, and Apache Kafka)
● Transformations

• Batch
• Streaming (e.g., windowing, late arriving data)
• Language
• Ad hoc data ingestion (one-time or automated pipeline)

● Data acquisition and import
● Integrating with new data sources

2.3 Deploying and operationalizing the pipelines. Considerations include:
● Job automation and orchestration (e.g., Cloud Composer and Workflows)
● CI/CD (Continuous Integration and Continuous Deployment)

3. Storing the data (20%)

3.1 Selecting storage systems. Considerations include:
● Analyzing data access patterns
● Choosing managed services (e.g., Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)
● Planning for storage costs and performance
● Lifecycle management of data

3.2 Planning for using a data warehouse. Considerations include:
● Designing the data model
● Deciding the degree of data normalization
● Mapping business requirements
● Defining architecture to support data access patterns

3.3 Using a data lake. Considerations include:
● Managing the lake (configuring data discovery, access, and cost controls)
● Processing data
● Monitoring the data lake

3.4 Designing for a data mesh. Considerations include:
● Building a data mesh based on requirements by using Google Cloud tools (e.g., Dataplex, Data Catalog, BigQuery, Cloud Storage)
● Segmenting data for distributed team usage
● Building a federated governance model for distributed data systems

4. Preparing and using data for analysis (15%)

4.1 Preparing data for visualization. Considerations include:
● Connecting to tools
● Precalculating fields
● BigQuery materialized views (view logic)
● Determining granularity of time data
● Troubleshooting poor performing queries
● Identity and Access Management (IAM) and Cloud Data Loss Prevention (Cloud DLP)

4.2 Sharing data. Considerations include:
● Defining rules to share data
● Publishing datasets
● Publishing reports and visualizations
● Analytics Hub

4.3 Exploring and analyzing data. Considerations include:
● Preparing data for feature engineering (training and serving machine learning models)
● Conducting data discovery

5. Maintaining and automating data workloads (18%)

5.1 Optimizing resources. Considerations include:
● Minimizing costs per required business need for data
● Ensuring that enough resources are available for business-critical data processes
● Deciding between persistent or job-based data clusters (e.g., Dataproc)

5.2 Designing automation and repeatability. Considerations include:
● Creating directed acyclic graphs (DAGs) for Cloud Composer
● Scheduling jobs in a repeatable way

5.3 Organizing workloads based on business requirements. Considerations include:
● Flex, on-demand, and flat rate slot pricing (index on flexibility or fixed capacity)
● Interactive or batch query jobs

5.4 Monitoring and troubleshooting processes. Considerations include:
● Observability of data processes (e.g., Cloud Monitoring, Cloud Logging, BigQuery admin panel)
● Monitoring planned usage
● Troubleshooting error messages, billing issues, and quotas
● Manage workloads, such as jobs, queries, and compute capacity (reservations)

5.5 Maintaining awareness of failures and mitigating impact. Considerations include:
● Designing system for fault tolerance and managing restarts
● Running jobs in multiple regions or zones
● Preparing for data corruption and missing data
● Data replication and failover (e.g., Cloud SQL, Redis clusters)