All MCQs

Operational metrics reveal bottlenecks and emerging capacity risks.
Trend monitoring supports proactive tuning before service deteriorates.

Audit trails show who changed or accessed what and when.
They support accountability, investigation, compliance, and reproducibility.

Retention balances analytical value, cost, performance, privacy, and legal duties.
The policy should define archival and secure disposal as well as retention periods.

Purpose limitation and data minimization reduce privacy risk.
Access, retention, consent, and lawful use must align with organizational obligations.

Masking changes the displayed representation according to permissions.
It reduces casual exposure but is not a substitute for encryption or access control.

Row-level security applies data-access rules inside the database engine.
Central enforcement protects data regardless of the reporting tool used.

Least privilege limits accidental and malicious misuse of data or systems.
Permissions should be role-based, reviewed, and removed when no longer needed.

Lineage reveals downstream dependencies that a schema change could break.
This supports safer planning, communication, testing, and release sequencing.

A catalog makes analytical assets searchable and understandable.
Ownership, definitions, certification, and lineage help users choose appropriate data.

A semantic layer makes data understandable and consistent for consumers.
Central logic reduces duplication and conflicting calculations across tools.

Certified models provide trusted foundations without eliminating analytical flexibility.
Publication, ownership, security, and metric standards control enterprise risk.

Tool selection should reflect functional, technical, organizational, and economic requirements.
A fashionable tool may still be unsuitable for the intended environment.