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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Designing the data model
  • 2. Deciding the degree of data normalization
  • 3. Defining architecture to support data access patterns
  • 4. Mapping business requirements
- Using a data lake
  • 1. Managing the lake (data discovery, access, cost controls)
  • 2. Monitoring the data lake
  • 3. Processing data
- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Analyzing data access patterns
  • 3. Lifecycle management of data
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
- Performing security considerations
  • 1. Data encryption
  • 2. Auditing, privacy, and compliance
  • 3. Identity and Access Management (IAM)
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Batch processing
  • 2. Data acquisition and import
  • 3. Integrating with new data sources
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. Processing logic
  • 6. AI data enrichment
- Designing data processing resources
  • 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 2. Cluster sizing and autoscaling
  • 3. Cost optimization
- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Performing data quality and validation checks
  • 3. Planning for monitoring and alerting
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
- Automating data processes
  • 1. Workflow orchestration
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Scheduling jobs
Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards

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Google Certified Professional Data Engineer Exam Sample Questions (Q207-Q212):

NEW QUESTION # 207
Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model. You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

Answer: D

Explanation:
Star schema is supported by Big Query but is not the most efficient form, if you should design a schema from scratch google recommend to use nested and repeated fields.
In this case, you already have done a migration of the schema and data, so it sounds good and with less effort to do partitioning by transaction date than to redesign the schema.
And other aspect to consider is that this is a data warehouse, so is sure that there is an ETL process and if you change the schema you must adapt the ETL process.


NEW QUESTION # 208
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?