Google Cloud Associate Data Practitioner Exam Prep

What's inside

1. Level 1: Exam Orientation and ETL, ELT and ETLTFree
   [Section 1: Data Preparation and Ingestion, item 1.1] Learners start by getting oriented. Google's exam guide is free on its certification page and is not reproduced here. Exam length, price and languages should be checked on Google's exam page. This course is independent of Google, is not endorsed by it and does not promise a pass. Learners then compare the ETL, ELT and ETLT methods of handling data.
2. Level 2: Choosing a Data Transfer ToolFree
   [Section 1: Data Preparation and Ingestion, item 1.1] Learners choose the right data transfer tool by comparing online transfer with Storage Transfer Service against offline transfer with Transfer Appliance, based on data volume, network bandwidth and time limits.
3. Level 3: Assessing Data Quality and Cleaning Data
   [Section 1: Data Preparation and Ingestion, item 1.1] Learners check data quality (completeness, accuracy, consistency, duplicates) and clean data using BigQuery SQL, Cloud Data Fusion and Dataflow.
4. Level 4: Data Formats and Data Structure Types
   [Section 1: Data Preparation and Ingestion, item 1.2] Learners tell apart CSV, JSON, Apache Parquet, Apache Avro and structured database tables, and sort use cases by whether they need structured, semi-structured or unstructured data.
5. Level 5: Choosing Storage Solutions and Location Types
   [Section 1: Data Preparation and Ingestion, item 1.2] Learners pick a storage solution from Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable and Spanner, and a location type: regional, dual-regional, multi-regional or zonal.
6. Level 6: Extraction Tools and Loading Data
   [Section 1: Data Preparation and Ingestion, item 1.2] Learners choose an extraction tool (Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion) and load data with gcloud and the bq CLI, Storage Transfer Service, BigQuery Data Transfer Service and client libraries.
7. Level 7: BigQuery SQL for Reports and Insights
   [Section 2: Data Analysis and Presentation, item 2.1] Learners write and run SQL queries in BigQuery that produce reports and pull out key insights from the data.
8. Level 8: Analysing Data in Colab Enterprise Notebooks
   [Section 2: Data Analysis and Presentation, item 2.1] Learners use Jupyter notebooks in Colab Enterprise to query BigQuery, analyse and chart the results, and answer business questions.
9. Level 9: Looker Dashboards, Looker Studio and LookML
   [Section 2: Data Analysis and Presentation, item 2.2] Learners create, change and share Looker dashboards that answer business questions, compare Looker with Looker Studio for different analytics needs, and edit simple LookML parameters in a data model.
10. Level 10: ML Use Cases, Project Planning and Remote LLMs
    [Section 2: Data Analysis and Presentation, item 2.3] Learners spot ML use cases suited to BigQuery ML or AutoML, plan a standard ML project (data collection, model training, model evaluation, prediction), and use pretrained Google large language models (LLMs) in BigQuery through a remote connection.
11. Level 11: BigQuery ML: Train, Evaluate, Predict, Register
    [Section 2: Data Analysis and Presentation, item 2.3] Learners use SQL to create, train and evaluate models with BigQuery ML, make predictions (inference) with them, and organise them in Model Registry.
12. Level 12: Selecting Transformation Tools for Pipelines
    [Section 3: Data Pipeline Orchestration, item 3.1] Learners choose a transformation tool from Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer and Dataform to meet business requirements, decide when ELT or ETL fits, and pick the products needed for a basic transformation pipeline.
13. Level 13: Scheduling and Orchestrating Data Tasks
    [Section 3: Data Pipeline Orchestration, item 3.2] Learners create and manage scheduled queries with BigQuery, Cloud Scheduler and Cloud Composer, and choose an orchestration solution from Cloud Composer, scheduled queries, Dataproc Workflow Templates and Workflows.
14. Level 14: Monitoring and Event-Driven Pipelines
    [Section 3: Data Pipeline Orchestration, item 3.2] Learners track a pipeline's progress in the Dataflow job UI, review logs in Cloud Logging and Cloud Monitoring, spot use cases for event-driven ingestion from Pub/Sub to BigQuery, and use Eventarc triggers in event-driven pipelines.
15. Level 15: Least-Privilege IAM for Data Services
    [Section 4: Data Management, item 4.1] Learners apply least-privilege access with Identity and Access Management (IAM), telling apart basic roles, predefined roles and permissions for BigQuery and Cloud Storage.
16. Level 16: Cloud Storage Access and Analytics Hub Sharing
    [Section 4: Data Management, item 4.1] Learners compare ways to control access to Cloud Storage (public or private access, and uniform bucket-level access) and decide when to share data with Analytics Hub, which Google now calls BigQuery sharing.
17. Level 17: Storage Classes, Lifecycle Rules and Archiving
    [Section 4: Data Management, item 4.2] Learners choose a Cloud Storage class based on how often data is accessed and how long it must be kept, set rules that delete objects after a set period in Cloud Storage and BigQuery, and evaluate Google Cloud services for archiving data.
18. Level 18: High Availability, Backups and Replication
    [Section 4: Data Management, item 4.3] Learners identify high availability and disaster recovery strategies for Cloud Storage and Cloud SQL: they compare Google-managed backup and recovery, decide when to use replication, and tell primary from secondary storage location types used for redundancy.
19. Level 19: Encryption Keys, Cloud KMS and Data Privacy
    [Section 4: Data Management, item 4.4] Learners apply security measures that support compliance with data privacy rules: they identify use cases for GMEK, CMEK and CSEK, explain what Cloud KMS does, and tell encryption in transit from encryption at rest.
20. Level 20: Timed Mock Exam (50 Questions, 120 Minutes) Timed mock
    [All Sections] A timed mock exam of 50 original scenario questions in 120 minutes, drawn from all four exam guide sections in proportion to their weights, with a 70% pass mark and every distractor explained.

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The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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