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| Section | Weight | Objectives |
|---|---|---|
| Data Quality Strategy and Governance | 20% | - Data quality strategy and policy - Roles and responsibilities - Integration with data governance |
| Data Quality Concepts and Principles | 15% | - Definition and importance of data quality - Data quality dimensions - Data quality in DMBOK framework |
| Data Quality Assessment and Profiling | 25% | - Assessment methods and tools - Defining quality rules and metrics - Data profiling techniques |
| Data Quality Improvement | 20% | - Root cause analysis - Data cleansing and standardization - Monitoring and continuous improvement |
| Tools, Techniques and Implementation | 20% | - Business value and ROI - Data quality in lifecycle management - Data quality tools and technologies |
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NEW QUESTION # 98
Which of the following is the best example of a 'documented data quality rule'?
Answer: B
Explanation:
Option D is the strongest example because it expresses a Data Quality requirement that is specific, measurable, associated with identified data, and explicitly connected to business need. It identifies the relevant data-transaction data from satellite systems-sets a measurable timeliness threshold of 12:05 a.m., and explains the business reason: ensuring that branches have access to actual customer balances for the required operating window. Published versions of this DAMA question identify the same choice.
This is fundamentally a Timeliness rule. DMBOK2 emphasizes that Data Quality requirements should be defined according to business expectations rather than vague aspirations. A useful rule needs sufficient precision to support objective measurement, exception reporting, and remediation.
Option E captures an important quality expectation-actual customer balances-but it lacks the same precise operational threshold. Option A is a security/confidentiality requirement rather than a Data Quality rule.
Options B and C primarily describe processing procedures and do not express the business-quality requirement as cleanly.
Once documented, the rule should be managed as metadata, assigned to an accountable steward, linked to its Critical Data Elements, monitored through metrics, and escalated when the specified threshold is breached.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Requirements; Data Quality Rules; Timeliness; Measurement; Business Impact; Metadata Management.
NEW QUESTION # 99
A financial transaction is captured correctly at 9:00 AM but does not become available to the fraud- monitoring system until 6:00 PM, although the business requirement is availability within five minutes.
Which Data Quality dimension is primarily violated?
Answer: B
Explanation:
The primary failure is Timeliness. The transaction may be completely accurate and complete, but it is not available within the period required by the consuming business process.
Timeliness evaluates whether data is available when needed for its intended use. The relevant threshold must therefore come from the business requirement rather than from an arbitrary technical target. In this scenario, the fraud-monitoring process requires the transaction within five minutes, while delivery occurs approximately nine hours later.
The root cause could exist in extraction frequency, integration queues, batch processing, network delays, source-system availability, or downstream ingestion. Lineage and operational metadata should be used to identify where the latency occurs.
Timeliness must also be distinguished from Currency. Currency asks whether information reflects a sufficiently recent real-world state; Timeliness asks whether data is delivered or available within the required period. A current transaction that arrives too late can therefore fail Timeliness even though the underlying value accurately represented reality when captured.
DAMA's revised DMBOK2 Chapter 13 recognizes both Timeliness and Currency as separate standard dimensions.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Timeliness; Currency; Data Quality Requirements; Data Integration; Operational Monitoring.
NEW QUESTION # 100
A search engine database is populated by a web crawler or spider software that usually processes as:
Answer: C
Explanation:
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.
NEW QUESTION # 101
The process of translating plain text into complex codes to hide privileged information is:
Answer: D
Explanation:
The process is Encryption. DAMA-DMBOK2 defines encryption as converting readable plaintext into coded information so that privileged or sensitive content cannot be understood without the appropriate decryption mechanism. The DMBOK2 security section specifically describes encryption as translating plain text into complex codes to hide privileged information and also notes its use in protecting transmission integrity and validating identity.
Encryption supports confidentiality both at rest and in transit. Depending on implementation, organizations may use symmetric encryption, asymmetric or public-key encryption, hashing for particular integrity or authentication functions, and other cryptographic mechanisms.
The crucial distinction is that encryption deliberately changes the representation of information according to a cryptographic algorithm and key structure. Encapsulation, enhancement, elimination, and exaggeration are not substitutes for cryptographic protection.
Within the wider DAMA framework, encryption should be applied according to classification and risk.
Highly sensitive information may require stronger cryptographic controls, restricted key management, rotation procedures, and separation of duties.
Data Quality and Data Security intersect at integrity: unauthorized changes may produce inaccurate data even when availability remains unaffected. Security controls therefore protect not merely secrecy but the reliability and trustworthiness of managed information.
Reference Topics: DAMA-DMBOK2 Chapter 7 - Encryption; Confidentiality; Data Integrity; Cryptographic Controls; Security Classification.
NEW QUESTION # 102
A company converts "Street", "St.", and "Str" to a single approved address representation before customer matching. This is an example of:
Answer: D
Explanation:
The activity is Standardization. Standardization converts semantically equivalent values into a consistent representation so they can be compared, matched, integrated, and interpreted reliably.
In the scenario, Street, St., and Str may represent the same address concept but differ syntactically.
Converting them to an approved standard reduces superficial variation before entity matching.
Standardization is commonly used for names, addresses, telephone numbers, units of measure, dates, product descriptions, geographic references, abbreviations, and coded values. It frequently precedes matching and de- duplication because entity-resolution algorithms perform more effectively when predictable representational differences have already been normalized.
Reference Data and Metadata Management are important supporting disciplines. Reference Data can define approved abbreviations or formats, while metadata documents transformation rules and intended meaning.
Standardization does not prove that the resulting address is factually accurate. It improves consistency and comparability. An address can be perfectly standardized yet belong to the wrong customer.
Therefore, standardization should be combined with validation, verification, matching, and source- improvement controls where appropriate.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Standardization; Cleansing; Matching; Consistency; Reference Data Management.
NEW QUESTION # 103
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