Data-Quality-10-Developer-Specialist Sample Questions Answers

Labeler

Decision

Standardizer

Parser

Number of Columns in the file

Number of Rows in the source data

Amount of memory and speed of the processor on the server

All of the above

Consolidation

Match and Association

Key Gen and Consolidation

Key Gen, Association and Consolidation

Upper Case

Lower Case

Toggle merge Case

Sentence Case

Threshold values

Scoring methods

Match Field Inputs

Weights

Only mappings using a Case Converter and a Standardizer can be used on data in Excel.

Only mappings using non active transformations can be used in DQ for Excel.

All DQ transformations, including Matching and Consolidation can be used in DQ for Excel.

All DQ and Core Transformations can be used in DQ for Excel, there is no limitation.

By running the DQ Client installer

The Analyst defines the table in the Analyst tool, and the mappings in the Developer tool create and populate the appropriate tables

The Analyst populates and creates the table in the Analyst tool

The Administrator needs to create a DQA Service and associate it with the staging database

The Developer can only review Profiles created by the Analyst but can not create any profiles themselves

The Developer can take Analyst built profiles with rules and generate a mapping ensuring lossless translation of Information between specification and Implementation.

Comments, Tags and Rules can only be applied to profiles in the Analyst Tool

Only one Tag can be applied to each object in a project.

No standardization is necessary, map input fields to the match transformation

Standardize address fields and use address line, locality, region and postcode fields as input to the match transformation

Standardize address fields and use the discrete address component fields, locality, region and postcode fields as input to the match transformation

Standardize address fields, create a group key field which contains parts of the address fields and only use the group key for address matching

Column profiling automatically recognizes all functional and other dependencies in all tables, in particular the primary keys

Column profiling is the process of looking at every distinct value to determine the true minimum, maximum, data type, pattern and other important information about each attribute

Column profiling identifies the attributes for referential integrity

Column profiling is the process of creating a normalized schema for your target system

Those changes are automatically propagated and managed through domain settings.

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

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

None of the above.

Use a reference table with the firstname as the valid column and the gender in the second column. Then choose to Replace Reference Table Matches with Valid Values when applying the reference table in a strategy in the Standardizer. Use the firstname as input.

Use a reference table with the gender as the valid column and the firstname in next column. Then choose to Replace Reference Table Matches with Valid Values when applying the reference table in a strategy in the Standardizer. Use the firstname as input.

Use a decision and create an If then else rule eg: If fname = 'Jennifer' then gender: = 'F' Endif

Use a reference table with the gender as the valid column and the firstname in next column. Then choose to Replace Reference Table Matches with Custom Strings when applying the reference table in a strategy in the Standardizer. Use the firstname as input.

Rules

Reference tables

Demonstration mappings

Demonstration data objects

Profiles

Company Executives & IT

IT & Business

IT & Administration

Sales & Marketing

The Address Validator transformation searches the address reference data and returns all addresses that are possible matches with the input address.

The Address Validator transformation searches the address reference data and returns the most likely candidate address.

The Address Validator transformation parses data into the suggested address fields without performing validation.

The Address Validator transformation will return various cleansed standardized versions of the input record for the user to choose from.

Scorecarding

Matching

Grading

Standardization

Column Profiling, Primary Key Inference, Dependency Inference

Column and Join Profiling only

Column, Join Analysis, Mid Stream and Comparative Profiling

Column, Join Analysis, Mid Stream, Comparative, Primary and Foreign Key and Overlap Profiling

IDQ field level matching does not utilize grouping

When field level matching is performed, the records within each group will be compared against each other

When field level matching is performed, matching will be performed across multiple groups in a single match transformation.

When field level matching is performed, matching will not be performed across groups, therefore it is imperative grouping is performed on a complete and accurate field(s)