Explanation:

Why C is Correct?

Global user distribution requires a licensing model that accommodates:

Geographic variability (e.g., time zones, peak usage times).

ATR (Active User Ratio) compliance: Ensures cost efficiency by matching licenses to actual usage patterns.

Tableau’s ATR Guide emphasizes this for multinational deployments.

Why Other Options Are Incorrect?

A. Cheapest licenses: May violate ATR or leave regions under-licensed.

B. Ignoring ATR: Leads to over-purchasing (e.g., unused licenses) or compliance penalties.

D. IT preferences: Doesn’t account for business needs or global scalability.

Key Steps for Multinational Licensing:

Analyze user activity per region (via Admin Insights).

Select Core-based licensing (flexible for global teams) or Named User (fixed roles).

Monitor ATR quarterly: Adjust licenses to maintain compliance (e.g., 1:3 ratio).

Reference:

Tableau’s Global Licensing Best Practices.

Final Note:

C is the only strategy balancing cost and compliance. Options A/B/D risk overspending or non-compliance. Always track usage metrics post-deployment.

Explanation:

Why C is Correct?

Network shared storage (e.g., NFS, SMB, or cloud storage like S3) is required for an external file store because:

All Tableau Server nodes must access the same files (e.g., extracts, backups).

Centralized storage ensures consistency across the cluster.

Tableau’s External File Store Guide mandates this setup.

Why Other Options Are Incorrect?

A. FTP access: Tableau Server cannot use FTP for its file store (only shared storage protocols).

B. SSL for Tableau Server: Secures data in transit but doesn’t enable file store access.

D. Increasing virtual memory: Unrelated—file store access depends on network permissions, not RAM.

Key Steps for External File Store Setup:

Configure shared storage:

NFS/SMB: Mount the share on all nodes at the same path (e.g., /mnt/tableau).

Cloud (S3, Azure Blob): Use Tableau’s cloud storage integration.

Set permissions:

Ensure the Tableau service account has read/write access.

Reference:

Tableau’s Cluster Storage Requirements.

Final Note:

C is the only solution that ensures cluster-wide access. Options A/B/D misrepresent technical requirements. Always test connectivity from all nodes.

✅ Explanation:

When transitioning from on-premises Tableau Server to Tableau Cloud, the top priority should be performing a thorough audit of current dashboards and data sources for compatibility with Tableau Cloud. This ensures that data source connectivity (e.g., on-premises databases via Tableau Bridge), authentication (OAuth, SAML), and content dependencies are validated before migration. Identifying and resolving compatibility issues upfront reduces the risk of broken dashboards, failed extracts, and user access problems post-migration. A comprehensive audit also helps categorize content by business criticality, enabling a phased migration—starting with less critical assets for testing—and reducing overall migration risk.

Why other options are incorrect:

A. Migrate all data and dashboards at once:
This "big bang" approach is high-risk. A single error can disrupt all users, causing significant downtime and data access failures. A phased migration with validation is far safer and more manageable.

C. Prioritize migration of least used dashboards:
This is a secondary testing strategy, not the primary priority. Compatibility assessment must come first to ensure the environment itself is ready for any content migration.

D. Discontinue on-premises immediately:
This is a dangerous approach that can lead to complete business disruption. Without proper testing and a fallback plan, users may lose access to critical data and reports, harming decision-making and operations.

🔗 References

Tableau Cloud Migration Guide (Salesforce): Recommends auditing content and data sources for compatibility before migration to avoid post-migration failures.

IIA Standard 2201 – Planning the Engagement: Emphasizes the importance of pre-migration planning and compatibility validation to mitigate operational risks.

Explanation:

Why B is Correct?

Complex calculated fields, excessive filters, and parameters are among the most common causes of slow workbook performance. These elements increase the computational load on Tableau’s rendering engine (VizQL)

. Optimizing calculations (e.g., replacing complex IF statements with Boolean logic) and reducing unnecessary filters can significantly improve load times.

Tableau’s Performance Checklist recommends this as the first troubleshooting step before considering hardware or structural changes.

Why Other Options Are Less Immediate?

A. Upgrading hardware: This may help, but it’s costly and doesn’t address the root cause (inefficient workbook design).

C. Splitting the workbook: A last resort—it adds management overhead and may not fix underlying inefficiencies

D. Checking network speed: Rarely the issue for slow rendering—performance problems usually stem from workbook complexity or data source queries.

Steps to Optimize Workbook Performance:

Use Tableau’s Performance Recorder to identify bottlenecks.

Simplify calculations:
Replace nested IFs with CASE or Boolean logic.
Use Level of Detail (LOD) expressions sparingly.

Reduce filters/parameters:
Limit cross-workbook filters.
Use parameters only when necessary.
Test incremental changes to measure improvements.

Reference:
Tableau’s Optimizing Workbooks Guide prioritizes calculation and filter optimization.

Final Note:
Always start with B—workbook optimization is free and often resolves performance issues. If problems persist, then investigate hardware (A) or data sources.

Explanation:

A comprehensive load test must simulate a realistic workload that reflects actual production usage patterns. Including a diverse mix of user activities—such as viewing dashboards (engaging VizQL Server), publishing workbooks (engaging Data Server), and performing data refreshes (engaging Backgrounder)—ensures the test evaluates performance across all Tableau Server processes. This approach helps identify bottlenecks that may only appear under specific workload conditions, providing a complete picture of server capacity and performance. For example, a server may handle dashboard views well but struggle with concurrent extract refreshes. Testing only one activity type would miss this critical insight, leading to inaccurate capacity planning and potential production failures. A representative workload mix is the foundation of meaningful load testing.

Why Other Options Are Incorrect

A. Largest dashboards only: This approach ignores other critical workloads like extract refreshes and publishing, providing an incomplete view of server performance and potentially missing bottlenecks in other processes.

C. Extract refreshes only: This neglects interactive user activities and rendering performance, which are often the primary user-facing workloads that directly impact user satisfaction.

D. Small, controlled group: This does not simulate peak production loads or concurrency levels, making it insufficient for capacity planning and performance validation.

References

Tableau Load Testing: Representative workload mix.
Tableau Server Performance Tuning: Comprehensive evaluation of all activities.
Tableau Server Administrator Guide: Load test planning.