All MCQs

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

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

Date partitions support targeted loads, retention, maintenance, and pruning.
They make very large tables easier to operate and scale.

Analytical queries often read a few columns across many rows.
Column-oriented storage improves compression, batch processing, and scan performance.

Direct remote access couples analytical performance to operational systems and networks.
A staged or replicated design usually provides better isolation and control.

SQL Server Agent executes scheduled jobs, alerts, and workflows.
Warehouse teams use it to automate loads, backups, and maintenance tasks.

SSRS is designed for parameterized, printable, and scheduled reporting.
It complements interactive dashboards with structured report delivery.

SSAS provides tabular and multidimensional analytical models.
It centralizes business calculations, relationships, hierarchies, and security.

SSIS is Microsoft’s platform for data integration and workflow orchestration.
It supports extraction, cleansing, transformation, loading, and scheduling patterns.

The Database Engine provides relational storage, indexing, security, and query processing.
It commonly hosts warehouse facts, dimensions, staging, and control tables.

Virtualization combines data through views or semantic access rather than full physical consolidation.
It may improve agility but can depend heavily on source availability and performance.

Federation presents a unified analytical view while data remains in multiple systems.
It can reduce migration effort but increases integration and governance complexity.