Data Warehousing and Business Intelligence MCQs

The middle tier translates warehouse data into analytical structures and business logic.
Cubes and semantic models are typical components of this tier.

A semantic model translates technical structures into reusable business concepts.
It improves consistency, usability, and centralized calculation logic.

Type 1 replaces the existing attribute value.
It is suitable for corrections when historical versions are not required.

Different metric definitions create contradictory analytical results.
A governed semantic layer and shared calculations help restore consistency.

Warehouses support analytical scans, aggregation, and historical comparison.
Operational databases prioritize reliable and fast transaction processing.

Warehouse changes are generally introduced through managed integration processes.
Operational systems are updated directly by business transactions.

Deployment makes the solution operational in a controlled production environment.
Scheduling, permissions, monitoring, training, and rollback planning are essential.

The top tier is where users consume and interact with analytical information.
It includes reporting, visualization, and self-service BI applications.

Tabular models organize data in familiar tables and relationships.
They commonly use DAX for calculations and VertiPaq for compressed in-memory storage.

An operational data store integrates recent data for operational reporting.
It usually holds less history and changes more frequently than a warehouse.

Strategic decisions have long-term, organization-wide consequences.
They benefit from historical trends, forecasts, and integrated performance measures.

Warehouse workloads emphasize reads, joins, and aggregations over substantial history.
They differ from the short write-heavy transactions common in OLTP.