Lack of automated monitoring represents serious risks, including compliance risk.
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B
Q: 2
Decentralized informality can be made more formal through a documented series of connections and
accountabilities via a RACI matrix.
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A
Q: 3
Normalisation is the process of applying rules in order to organise business complexity into stable
data structures.
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A
Q: 4
Traditional tool sin data visualtization have both a data and a graphical component. Advanced
visualization and discovery tools use in-memory architecture to allow users to interact with the data.
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A
Q: 5
XML is the abbreviation for standard mark-up language.
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A
Q: 6
Big data primarily refers specifically to the volume of the data.
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B
Q: 7
Data architect: A senior analyst responsible for data architecture and data integration.
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Q: 8
A dimensional physical data model is usually a star schema, meaning there is one structure for each
dimension.
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B
Q: 9
Data Governance is at the centre if the data management activities, since governance is required for
consistency within and balance between functions.
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B
Q: 10
Data security internal audits ensure data security and regulatory compliance policies are followed
should be conducted regularly and consistently.
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A
Q: 11
Development of goals, principles and policies derived from the data governance strategy will not
guide the organization into the desired future state.
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A
Q: 12
Data parsing is the process of analysing data using pre-determined rules to define its content or
value.
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B
Q: 13
Data professional should not balance the short-term versus long-term business interests.
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B
Q: 14
A e-discovery readiness assessment should examine and identify opportunities for the commercial
response program.
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B
Q: 15
The best DW/BI architects will design a mechanism to connect back to transactional level and
operational level reports in an atomic DW.
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B
Q: 16
Effective document management requires clear policies and procedures, especially regarding
retention and disposal of records.
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B
Q: 17
Data professionals involved in Business Intelligence, analytics and Data Science are often responsible
for data that describes: who people are; what people do; where people live; and how people are
treated. The data can be misused and counteract the principles underlying data ethics.
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B
Q: 18
DAMA International’s Certified Data Management Professional (CDMP) certification required that
data management professionals subscribe to a formal code of ethics, including an obligation to
handle data ethically for the sake of society beyond the organization that employs them.
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A
Q: 19
Examples of concepts that can be standardized within the data quality knowledge area include:
