Tableau Features and Optimization Questions
Addresses advanced Tableau capabilities and performance tuning for dashboards and server deployments. Topics include calculated fields, parameters, table calculations, and level of detail expressions with when to use fixed, include, and exclude forms. Covers optimization strategies for Tableau workbooks and Tableau Server such as extract management, efficient data sources, query reduction, dashboard best practices, and row level security implementation. Also includes monitoring and tuning of server resources and extract refresh strategies to ensure responsive analytics at scale.
HardTechnical
0 practiced
A senior stakeholder demands a sub-3-second load time for a complex workbook that currently averages 12 seconds. The workbook includes multiple LODs, table calculations, and custom SQL against a large warehouse. Provide a prioritized plan of technical changes (short-term and long-term) to meet the SLA and justify each change by expected impact.
EasyTechnical
0 practiced
When should you use data blending instead of joining tables in Tableau? Provide two realistic examples of situations where blending is the better choice, and describe the performance implications of each.
HardTechnical
0 practiced
For an org with 2,000 concurrent users and heavy ad-hoc queries, compare the pros/cons and cost-performance of three approaches: (1) live connections to a highly optimized analytical warehouse, (2) centralized incremental extracts refreshed hourly, and (3) per-user/session pre-aggregated marts. Make a recommendation with justification.
MediumTechnical
0 practiced
A business user reports inconsistent numbers between Tableau and their Excel exports. Describe an investigation plan to find the cause (data model, aggregations, date handling, nulls, filters) and the steps to correct discrepancies and prevent future confusion.
EasyTechnical
0 practiced
A dataset has 200 million rows and needs near-real-time reporting every hour. Explain the trade-offs between using a live connection and using extracts (Hyper) in Tableau. For this scenario, recommend an approach and describe how you would ensure both performance and data freshness.
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