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1478-C2090-304: IBM InfoSphere QualityStage v9.1 Solution Developer
#Question
Why is change management important in data quality initiatives?
A.
To increase resistance
B.
To enhance user stress
C.
To minimize errors
D.
To avoid documentation
Correct Answer:
C
Change management is important in data quality initiatives to minimize errors during transitions and adaptations to new data quality practices within the organization.
#Question
How does implementing a data quality framework impact stakeholder engagement?
A.
Decreases trust
B.
Enhances communication
C.
Reduces involvement
D.
Limits transparency
Correct Answer:
B
Implementing a data quality framework enhances communication among stakeholders, fostering better collaboration and engagement regarding data practices.
#Question
Which aspect of data quality is assessed through the 'Consistency' dimension?
A.
Complete values
B.
Timeliness of data
C.
Accuracy of data
D.
Uniformity across datasets
Correct Answer:
D
The 'Consistency' dimension of data quality assesses the uniformity of data values across datasets, ensuring that they do not conflict with one another.
#Question
What advantage does integrated data profiling provide in QualityStage?
A.
Leads to data fragmentation
B.
Ensures clarity in workflows
C.
Increases storage needs
D.
Enhances insight generation
Correct Answer:
D
Integrated data profiling enhances insight generation, allowing users to better understand data and its quality attributes across the organization.
#Question
How does QualityStage support the identification of data relationships?
A.
By merging records
B.
Through data profiling
C.
By standardizing formats
D.
By categorizing data
Correct Answer:
B
QualityStage supports the identification of data relationships through data profiling, which helps analyze and categorize data based on relevant characteristics.
#Question
Which practice is essential for maintaining high data quality standards in QualityStage?
A.
Automatic processing
B.
Time management
C.
Simple data entry
D.
Regular training
Correct Answer:
D
Regular training is essential to maintain high data quality standards in QualityStage, ensuring that users are knowledgeable about data quality practices.
#Question
What is the primary goal of implementing data quality metrics in QualityStage?
A.
To increase errors
B.
To track performance
C.
To enhance reporting
D.
To limit data types
Correct Answer:
B
The primary goal of implementing data quality metrics in QualityStage is to track performance and identify areas needing improvement in data management.
1478-C2090-304: IBM InfoSphere QualityStage v9.1 Solution Developer
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