Checklist progress
0/373Learned
Tableau Architect
Study Checklist
Checklist progress
0/373Learned
1.1.1 Evaluate requirements for users and their role distributions
0/5
1.1.2 Identify relevant constraints and requirements, including future growth
0/5
1.1.3 Identify requirements for and recommend a strategy for licensing, including Authorization-to-Run
0/8
1.1.4 Assess the need for high availability and disaster recovery
0/6
1.1.5 Map the features and capabilities of the Tableau Server Add-Ons to requirements
0/8
1.2.1 Plan and implement Tableau Bridge
0/5
1.2.2 Plan and implement authentication
0/6
1.2.3 Plan and implement automated user provisioning, including System for Cross-Domain Identity Management
0/4
1.2.4 Troubleshoot advanced configuration issues
0/5
1.2.5 Plan and implement multi-sites using Tableau Cloud Manager
0/2
1.3.2 Plan a migration of Tableau Server to Tableau Cloud
0/5
1.3.3 Plan a migration from Windows to Linux
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1.3.4 Plan a migration from Linux to Windows
0/3
1.3.5 Plan a migration from one identity store to another
0/3
1.3.6 Plan a consolidation of multiple Tableau servers or sites into fewer servers or sites
0/3
1.3.7 Plan a migration from one Tableau Server environment to another
0/5
1.3.8 Create scripts for migration
0/2
1.3.9 Use the Tableau Content Migration Tool
0/5
1.4.1 Specify process counts
0/7
1.4.2 Specify node count
0/3
1.4.3 Specify service-to-node relationships (node roles), including service isolation and service colocation
0/4
1.4.4 Specify when to use external services
0/4
1.5.1 Recommend an appropriate identity store and authentication configuration
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1.5.2 Recommend specific configuration keys and values to suit a given use case
0/3
1.5.3 Recommend a configuration to address security requirements such as encryption at rest and encryption over the wire
0/5
1.5.4 Recommend hardware and network specifications
0/3
1.5.5 Create a disaster recovery strategy
0/5
1.5.6 Design and recommend virtual connections and data policies for centralized row-level security
0/5
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Tableau Server's Data Engine (Hyper) does not require increasing process counts to improve performance. Instead, it automatically scales by utilizing all available CPU cores and memory on a node, making hardware resource allocation the primary driver for extract query performance.