Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!
Prepare and test your skills
Prepare and test your skills
Google Cloud service
Topic in 36 exam sections
Also called: AlloyDB, App Hub API, ASM, AWS EC2, BigLake, BQDT, Cloud DLP, Cloud SQL for SQL Server, CTS, DLP API, DTS, Endpoints, gcloud, gcloud CLI, GCS, GDC, GKE clusters, Google Cloud support package, Google Cloud Video Intelligence, Google SecOps, Google Vertex AI, IAM, Identity and Access Management, Kubernetes Engine, Logging, Managed Service for Prometheus, Monitoring, Organization Policy, support packages, Toolbox, Traffic Director, Video Intelligence, Vision, Vision API, VPC
Google Cloud storage where files are copied before loading into BigQuery when using files as an intermediate vehicle.
Study guide
Section 1: Data Preparation and Ingestion
1.1 Prepare and process dataStudy guide
Section 1: Data Preparation and Ingestion
Differentiate between different data manipulation methodologies (e.g., ETL, ELT, ETLT)Study guide
Section 1: Data Preparation and Ingestion
Choose the appropriate data transfer tool (e.g., Storage Transfer Service, Transfer Appliance)Study guide
Section 1: Data Preparation and Ingestion
Assess data qualityStudy guide
Section 1: Data Preparation and Ingestion
Conduct data cleaning (e.g., Cloud Data Fusion, BigQuery, SQL, Dataflow)Study guide
Section 1: Data Preparation and Ingestion
1.2 Extract and load data into appropriate Google Cloud storage systemsStudy guide
Section 1: Data Preparation and Ingestion
Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion)Study guide
Section 1: Data Preparation and Ingestion
Select the appropriate storage solution (e.g., Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner)Study guide
Section 1: Data Preparation and Ingestion
Classify use cases into having structured, unstructured, or semi-structured data requirementsStudy guide
Section 1: Data Preparation and Ingestion
Load data into Google Cloud storage systems using the appropriate tool (e.g., gcloud and BQ CLI, Storage Transfer Service, BigQuery Data Transfer Service, client libraries)Study guide
Study guide
Section 2: Data Analysis and Presentation
2.1 Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooksStudy guide
Section 2: Data Analysis and Presentation
Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise)Study guide
Section 2: Data Analysis and Presentation
Analyze data to answer business questionsStudy guide
Section 2: Data Analysis and Presentation
2.2 Visualize data and create dashboards in Looker given business requirementsStudy guide
Section 2: Data Analysis and Presentation
Create, modify, and share dashboards to answer business questionsStudy guide
Section 2: Data Analysis and Presentation
Compare Looker and Looker Studio for different analytics use casesStudy guide
Section 2: Data Analysis and Presentation
2.3 Define, train, evaluate, and use ML modelsStudy guide
Section 2: Data Analysis and Presentation
Use pretrained Google large language models (LLMs) using remote connection in BigQueryStudy guide
Section 2: Data Analysis and Presentation
Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction)Study guide
Section 2: Data Analysis and Presentation
Execute SQL to create, train, and evaluate models using BigQuery MLStudy guide
Section 2: Data Analysis and Presentation
Perform inference using BigQuery ML modelsStudy guide
Study guide
Section 3: Data Pipeline Orchestration
3.1 Design and implement simple data pipelinesStudy guide
Section 3: Data Pipeline Orchestration
Select a data transformation tool (e.g., Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform) based on business requirementsStudy guide
Section 3: Data Pipeline Orchestration
Evaluate use cases for ELT and ETLStudy guide
Section 3: Data Pipeline Orchestration
Create and manage scheduled queries (e.g., BigQuery, Cloud Scheduler, Cloud Composer)Study guide
Section 3: Data Pipeline Orchestration
Monitor Dataflow pipeline progress using the Dataflow job UIStudy guide
Section 3: Data Pipeline Orchestration
Identify use cases for event-driven data ingestion from Pub/Sub to BigQueryStudy guide
Section 3: Data Pipeline Orchestration
Use Eventarc triggers in event-driven pipelines (Dataform, Dataflow, Cloud Functions, Cloud Run, Cloud Composer)Study guide
Study guide
Section 4: Data Management
4.1 Configure access control and governanceStudy guide
Section 4: Data Management
Establish the principles of least privileged access by using Identity and Access Management (IAM)Study guide
Section 4: Data Management
Compare methods of access control for Cloud Storage (e.g., public or private access, uniform access)Study guide
Section 4: Data Management
Determine when to share data using Analytics HubStudy guide
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