How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Amazon.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Amazon during technical interviews.

Compare Tableau live vs extract and filters

Quick Overview

This question evaluates competency in BI data architecture and large-scale dashboard performance, covering extract vs live connections, refresh and incremental strategies, query pushdown versus in-tool processing, row-level security, filter order-of-operations, performance tuning, and platform migration mapping within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess trade-off reasoning across data freshness, storage, query performance and security for a 100M-row fact table, combining practical application-level decisions (implementation and tuning) with conceptual understanding (filter ordering, calculations, and migration pitfalls) when moving between BI platforms.

Scenario

You are building an interactive dashboard over a 100M-row fact table. Compare Tableau connection options and performance behaviors for this scale, and map the concepts to Amazon QuickSight for a potential migration.

Part A — Tableau: Live vs Extract for 100M rows

Discuss the trade-offs between Live and Extract connections, specifically addressing:

Refresh cadence and data freshness.
Query pushdown to the database vs Tableau Hyper.
Extract size and storage considerations.
Incremental refresh design (late-arriving data, updates vs inserts).
Row-level security (RLS) enforcement and data leakage risks.
When each option is preferable for a 100M-row fact table.

Part B — Tableau Order of Operations and Effects

Explain Tableau's order of operations and how it affects the following:

Data source filters
Context filters
Dimension filters
Measure filters
TOP/N filters
Table calculations
LOD expressions: FIXED, INCLUDE, EXCLUDE

Part C — Tableau Performance Tuning Tactics

Explain how to tune performance for this dashboard by:

Selecting high-selectivity dimensions for context filters.
Using extract filters and pre-aggregation.
Avoiding expensive table calculations when possible.
Choosing denormalization vs joins/relationships appropriately.

Part D — Mapping to Amazon QuickSight

Map the above ideas to Amazon QuickSight analogs and note one migration pitfall:

SPICE vs Direct Query (including refresh/incremental, pushdown, size).
Row-level security approaches.
Filters and calculation analogs.
One common pitfall when migrating a Tableau workbook to QuickSight.

Quick Overview

Part A — Tableau: Live vs Extract for 100M rows

Discuss the trade-offs between Live and Extract connections, specifically addressing:

Refresh cadence and data freshness.

Query pushdown to the database vs Tableau Hyper.

Extract size and storage considerations.

Incremental refresh design (late-arriving data, updates vs inserts).

Row-level security (RLS) enforcement and data leakage risks.

When each option is preferable for a 100M-row fact table.

Compare Tableau live vs extract and filters

Quick Overview

Scenario

Part A — Tableau: Live vs Extract for 100M rows

Part B — Tableau Order of Operations and Effects

Part C — Tableau Performance Tuning Tactics

Part D — Mapping to Amazon QuickSight

Solution

Comments (0)

Compare Tableau live vs extract and filters

Quick Overview

Scenario

Part A — Tableau: Live vs Extract for 100M rows

Part B — Tableau Order of Operations and Effects

Part C — Tableau Performance Tuning Tactics

Part D — Mapping to Amazon QuickSight

Solution

Comments (0)