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Data-Quality-10-Developer-Specialist Data Quality 10: Developer Specialist Exam Question and Answers

Question # 4

Which of the following cannot be executed in BOTH the Analyst and the Developer Tool?

Options are :

A.

A rule created in the Analyst Tool

B.

A mapplet created in the Developer Tool and validated as a rule

C.

A mapplet created in the Developer Tool and validated as a mapplet

D.

A reusable rule created in the Analyst Tool

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Question # 5

When changes are made to a mapplet in IDQ 9.1 that has been integrated into PowerCenter, how are those changes propagated to the integrated mapplet?

A.

Those changes are automatically propagated and managed through domain settings.

B.

The user must manually re-export the IDQ mapplet to PowerCenter.

C.

The IDQ versioning system automatically synchs with PowerCenter and updates themapplet to the most current version.

D.

None of the above.

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Question # 6

Where does Scorecarding fit into the data quality process?

Options are :

A.

Not at all - Scorecardlng is a separate process

B.

At the start of the process only to assess current data quality levels

C.

At the start and during regular intervals of the process to measure on-going data quality levels

D.

At the end of the process only to prove the value of the data quality tool

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Question # 7

How much data can DQA handle?

Options are :

A.

2000 rows and 20 columns

B.

Million rows and 100 columns

C.

There are no built-in restrictions. Large deployments will require more hardware and database tuning

D.

Maximum 1 million rows and up to 10,000 columns

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Question # 8

The Pattern Parser is used in conjunction with which transformation?

Options are :

A.

Token Parser

B.

Standardizer

C.

Exception

D.

Labeler

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Question # 9

Which two are index key levels in Identity Matching? Choose 2 answers

A.

Narrow

B.

Limited

C.

Typical

D.

Extended

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Question # 10

When matching, is it always necessary to group data?

Options are :

A.

Only if the customer thinks it's a good idea

B.

Only if the quality of the data is poor

C.

Not always. If the dataset is small enough, grouping is not required

D.

Yes, it's always necessary

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