DAMA Updated DQ-1220 Exam Questions and Answers by ewan

Web-scale search-engine repositories commonly operate according to BASE: Basically Available, Soft State, Eventually Consistent. BASE is associated with distributed and many NoSQL data architectures where availability, horizontal scalability, and tolerance of distributed processing are prioritized over the immediate transactional consistency expected from traditional ACID systems.

A web crawler collects and indexes content continuously across very large distributed environments. Different nodes or indexes may temporarily contain slightly different versions of information while updates propagate. The design is acceptable because the system is expected to converge toward consistency rather than guarantee that every replica reflects the identical state at every instant. DAMA-aligned material explicitly contrasts BASE with traditional ACID approaches for large-scale distributed data processing.

ACID—Atomicity, Consistency, Isolation, Durability—is generally associated with transaction-oriented relational systems where individual transactions require strict integrity guarantees. Search indexing has different workload characteristics and can tolerate temporary inconsistency.

The Data Quality implication is that “consistency” must be evaluated according to architectural context. Temporary divergence can be expected behavior in an eventually consistent platform rather than evidence of a defect, provided convergence occurs within acceptable business thresholds.

Reference Topics: DAMA-DMBOK2 — Data Storage and Operations; Distributed Databases; NoSQL; ACID versus BASE; Availability; Consistency.

===============

Within the DMBOK2 framing used by this certification material, the expanded characteristics are Volume, Velocity, Variety/Variability, Viscosity, Volatility, and Veracity. Option E is therefore the only choice that correctly contains the DAMA terms represented in the question. DMBOK-oriented study material explains that the original three Vs—Volume, Velocity, and Variety—were expanded to include Variability, Viscosity, Volatility, and Veracity in the broader characterization.

Volume concerns data quantity; Velocity concerns the rate of creation and processing; Variety/Variability concerns differing structures and changing representations; Viscosity describes difficulty in using or integrating the data; Volatility concerns how quickly usefulness or meaning changes; and Veracity addresses credibility and trustworthiness.

These characteristics have direct Data Quality implications. Increased variety creates semantic and structural consistency challenges. Velocity reduces the time available for traditional validation. Volatility affects currency and timeliness. Veracity is directly concerned with reliability.

DAMA's key point is that Big Data does not reduce the need for management discipline. Its scale and complexity increase the need for metadata, governance, automated profiling, lineage, and statistically driven quality controls.

Reference Topics: DAMA-DMBOK2 Big Data and Data Science — Big Data Characteristics; Volume; Velocity; Variety/Variability; Viscosity; Volatility; Veracity; Chapter 13 — Scalable Data Quality.

===============

The organization should continue monitoring the quality metric. A successful remediation does not guarantee that the underlying process will remain controlled indefinitely.

Systems change, source applications are upgraded, personnel and suppliers change, reference values evolve, interfaces are modified, and business rules are revised. Any of these events can cause previously corrected defects to reappear.

A mature Data Quality lifecycle therefore treats improvement as continuous rather than as a one-time cleansing exercise. DAMA's revision of Chapter 13 specifically clarifies the Data Quality Improvement Lifecycle and links it to established continuous-improvement cycles.

Monitoring should confirm that the result remains within agreed thresholds and should provide trend information so deterioration can be detected before it creates significant business impact.

The frequency of monitoring may be reduced after a process demonstrates sustained stability, but the decision should be risk-based. Critical Data Elements generally justify stronger ongoing oversight.

The rule, threshold, owner, and measurement method should remain documented as metadata.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Monitoring; Control; Continuous Improvement; Metrics; Thresholds; Data Quality Scorecards.

===============

DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.

Category information classifies things—for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.

Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.

This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.

Data models also generate important metadata—definitions, relationships, domains and constraints—which becomes the basis for measurable Data Quality rules.

Reference Topics: DAMA-DMBOK2 Chapter 5 — Types of Data that are Modeled; Chapter 10 — Reference and Master Data; Chapter 13 — Data Quality Rules and Dimensions; Metadata Management.

===============