Data Quality Specialist
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Mapping - ansDefines the flow and transformation of data
Mapplet - ansReusable predefined process containing transformations
Transformation - ansObjects used to transform data
Ports - ansInput or output fields
Strategy - ansOperation that is applied to data in a transformation
Profiling - ansAnalysis of the content and structure of data
Transformation Pallet - ansAll transformations displayed in scroll bar on the left hand side
Mainline Editor - ansWhere mappings, profiles, workflows are created. Where
transformations are placed
Outline - ansShows dependencies of an object, this can be a transformation or rule
Object Explorer - ansShows model repository, projects, folders and data objects
Properties - ansShows configurations for transformations or mapplets / mappings
Data Viewer - ansRuns data through mapping and displays it.
Cheat Sheets - ansProvides step by step guide for a process or transformation configuration
guide
Model Repository - ansRelational database stores metadata for projects and folders
Projects - ansHighest level container that organized objects and processes also containing
nested folders
Read Permission - ansExecute rules, export data and export rules
Write Permission - ansDelete or edit the project
Grant Permission - ansExecute rules, export data, export rules and contains the ability to
assign others tasks
Logical Data Object - ansRegular object combining the can combine data sources behind the
scenes and provide one view
Content Set - ansModel Repository Object used to store reusable content
Aggregator Transformation - ansPerform aggregate calculations
Custom Data Transformation - ansProcesses data in unstructured / semi-structured file
formats
Expressions Transformation - ansPerforms low level calculations
Filter Transformation - ansUsed as condition statement for inclusion
Java Transformation - ansAllows Java syntax to be used in Informatica
Joiner Transformation - ansJoins heterogeneous sources
Router Transformation - ansRoutes rows conditionally
Rank Transformation - ansSets the condition for rows included in a rank
Sorter Transformation - ansUsed to sort data
Union Transformation - ansPerforms union all join between two data streams
Update Strategy Transformation - ansTags rows as insert update delete or reject
Read Transformation - ansReads from Logical Data Objects
Write Transformation - ansWrites to Logical Data Objects
Lookup Transformation - ansLooks up values and passes them to other objects
,Data Quality Specialist
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Case Transformation - ansChanges case of data
Merge Transformation - ansConcatenates values
Address Validator Transformation - ansExamines input addresses and outputs corrected
address elements and validation information
Association Transformation - ansCreates links between records assigned to different match
clusters so they can be associated for consolidation
Comparison Transformation - ansEvaluates similarity between pairs of input values
Consolidation Transformation - ansCreates single consolidated record from cluster of
matched records
Decision Transformation - ansEvaluates conditions and creates outputs based on those
conditions
Key Generator - ansOrganized records into groups based on data values ahead of matching
Labeler Transformation - ansCreates labels that describes characters or strings in each field
Classifier Transformation - ansClassifies or labels according to classify models
Match Transformation - ansCalculates the degree of similarity between input records and
creates scorecard
Parser Transformation - ansFields with multiple information type and create new output
fields for each type
Standardizer Transformation - ansRemoves noise and creates standardized values
Weighted Average Transformation - ansReads scores generated by comparison and generates
weighted values based on inputs
Exception Transformation - ansUsed to create table required for exception and duplicate
management process
Distinct Values - ansUnique values within a column
Standardization - ansAddresses issues identified through profiling on correct completeness,
conformity, consistency, enhance and enrich information
Cleanse and Transform - ansRemoves values, Alters values and creates new values
Enrich - ansAppend bad data
Data Quality Precision - anssum of ports plus their join characters
Classifier - ansability to classify data based on classification model stored as part of a content
set
Classic Matching - ansSpecify what field you want to apply along with the algorithm.
Requires standardized inputs
Identity Matching - ansDelivers next generation linguistic and statistical matching algorithms
and enables business user to deliver accurate matches. Emulates a human experts ability to
determine a match and Delivers highest possible reliability
Threshold Value - ansPicks how exact or close the match of two rows will be up to 1 which is
a perfect match
Group Key - ansAllows records to be compared that only have the same group key,
ultimately reducing pairs and improving performance
,Data Quality Specialist
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Key Creation Types - ansString, Soundex, NYSIIS
Key Creation String - ansString first or last number of characters
Key Creation Soundex - ansGenerate alphanumeric code based on how a word sounds
Key Creation NYSIIS - ansConverts a word into its phonetic equivalent
Classic Match Strategies - ansEdit Distance, Jaro Distance, Bigram, Hamming Distance
Edit Distance - ansDerives match score for two values by calculating minimum cost of
transforming one string into another by insert, update, delete, replace
Jaro Distance - ansCalculates the similarity between two values if the first four characters are
the same. If the first four do not match score goes down by penalty property
Bigram - ansCalculates a match score by dividing the number of matched character pairs by
the total number of character pairs. Used for long text strings such as postal addresses
Hamming Distance - ansCalculates a match score by calculating the number of positions in
which characters differ between data strings. Use this when character position is a critical
factor such as telephone numbers, zip codes or product codes. Works well on strings of same
length.
Reverse Hamming - ansCalculates a match score by calculating the number of positions in
which characters differ between data strings reading from right to left. Use this when
character position is a critical factor such as telephone numbers, zip codes or product codes.
Works well on strings of same length.
Cluster ID - ansNumber that signifies records that match with each other will be assigned to
the same cluster
GroupKey - ansA key that is assigned to groups made from the string, soundex or NYSIIS
strategy. The field is the same Groupkey that was passed into the match transformation.
Cluster Size - ansNumber of records in a cluster, records that don't match with anything end
up in cluster sizes of 1
Row ID - ansVariation of Sequence ID passed into the Match Transformation
Driver ID - ansRecord in a cluster that is determined to be the driver or master record
Driver Score - ansScore calculated between a record and the Driver record
Link ID - ansIdentifies the record that pulled the current record in question into the cluster.
This does not have to be a master record
Link Score - ansScore between a record in question and the record that pulled the record in
question into the cluster
Match Cluster Analysis - ansShows statistics on the output from the match transformation,
consisting of Cluster ID, Groupkey, Clustersize, Row ID, Driver ID, Driver Score, Link ID,
Link Score
Workflow - ansGraphical representation defining a business process comprised of steps
Task - ansRuns unit of work in the workflow
Gateway - ansMakes decisions to split and merge paths in a workflow
Sequence flow - ansconnects workflow objects to specify the order that the Data Integration
Service (DIS) runs the objects
, Data Quality Specialist
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Human Task - ansContains steps that require human input to complete and can be used to fix
bad exceptions. Helps users participate in the business process
Assignment Task - ansValue to user defined workflow variable
Command Task - ansRuns single shell command, if successful returns 0 if not returns error
number
Mapping Task - ansRuns a mapping
Notification Task - ansSends out a notification or email to users
Task Performers - ansPeople who correct bad records
Token Parser - ansConsists of individual strings that match token sets or reference tables
Can a Token Parser standardize while parsing? - ansYes
Unparsed Output - ansNot matches found when parsing
Overflow - ansParsed but could not be send to an output port
Detailed Overflow - ansAllows users to see detail on which column overflowed
Token Parser Advantages - ansQuick to configure, Standardize as it parses, Multiple outputs
to the same output, Reverse parse, Append reference tables
Token Parser Disadvantages - ansNot sensitive, works better on unstructured data, output
types need to be well defined, Tries to use the first reference table, If a value is already parse
it overflows rather than parsing to subsequent tables
Pattern Parser - ansParses multiple strings to form patterns
What types of patterns can be used while parsing? - ansCustomer patterns, reference tables
and content set patterns
What is required when using a pattern parser? - ansThe input to the transformation must be
the output from the labeler transformation
Patterns are created in this transformation to be used in other transformations? - ansLabeler
How can you write patterns to a reference table or content set in order to be reused? -
ansRight click on a pattern and select send to reference table or new data domain
Pattern Parser Advantages - ansSpecify each pattern, Outputs can be determined on pattern
by basis, Multiple tokens output under same output column, Append reference tables and
content sets, Names of patterns that match content set patterns will auto parse
Pattern Parser Disadvantages - ansWorks better on structured data, Does not standardize as it
parses
Probabilistic Parsing Advantage - ansDoesn't need exact reference table matches since it can
make assumptions from a model
Model - ansSubset of read data where entity has been identified and provides a representation
for a data set used for training
How do you configure a probabilistic parser? - ansAdd a token parser, Add a strategy, Parse
tokens using probabilistic models, Rearrange ports and preview data, make required updates
Where can Analyst users review and update duplicate records? - ansAnalyst task inbox
The Analyst task inbox? - ansNotifies human tasks to users
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Mapping - ansDefines the flow and transformation of data
Mapplet - ansReusable predefined process containing transformations
Transformation - ansObjects used to transform data
Ports - ansInput or output fields
Strategy - ansOperation that is applied to data in a transformation
Profiling - ansAnalysis of the content and structure of data
Transformation Pallet - ansAll transformations displayed in scroll bar on the left hand side
Mainline Editor - ansWhere mappings, profiles, workflows are created. Where
transformations are placed
Outline - ansShows dependencies of an object, this can be a transformation or rule
Object Explorer - ansShows model repository, projects, folders and data objects
Properties - ansShows configurations for transformations or mapplets / mappings
Data Viewer - ansRuns data through mapping and displays it.
Cheat Sheets - ansProvides step by step guide for a process or transformation configuration
guide
Model Repository - ansRelational database stores metadata for projects and folders
Projects - ansHighest level container that organized objects and processes also containing
nested folders
Read Permission - ansExecute rules, export data and export rules
Write Permission - ansDelete or edit the project
Grant Permission - ansExecute rules, export data, export rules and contains the ability to
assign others tasks
Logical Data Object - ansRegular object combining the can combine data sources behind the
scenes and provide one view
Content Set - ansModel Repository Object used to store reusable content
Aggregator Transformation - ansPerform aggregate calculations
Custom Data Transformation - ansProcesses data in unstructured / semi-structured file
formats
Expressions Transformation - ansPerforms low level calculations
Filter Transformation - ansUsed as condition statement for inclusion
Java Transformation - ansAllows Java syntax to be used in Informatica
Joiner Transformation - ansJoins heterogeneous sources
Router Transformation - ansRoutes rows conditionally
Rank Transformation - ansSets the condition for rows included in a rank
Sorter Transformation - ansUsed to sort data
Union Transformation - ansPerforms union all join between two data streams
Update Strategy Transformation - ansTags rows as insert update delete or reject
Read Transformation - ansReads from Logical Data Objects
Write Transformation - ansWrites to Logical Data Objects
Lookup Transformation - ansLooks up values and passes them to other objects
,Data Quality Specialist
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Case Transformation - ansChanges case of data
Merge Transformation - ansConcatenates values
Address Validator Transformation - ansExamines input addresses and outputs corrected
address elements and validation information
Association Transformation - ansCreates links between records assigned to different match
clusters so they can be associated for consolidation
Comparison Transformation - ansEvaluates similarity between pairs of input values
Consolidation Transformation - ansCreates single consolidated record from cluster of
matched records
Decision Transformation - ansEvaluates conditions and creates outputs based on those
conditions
Key Generator - ansOrganized records into groups based on data values ahead of matching
Labeler Transformation - ansCreates labels that describes characters or strings in each field
Classifier Transformation - ansClassifies or labels according to classify models
Match Transformation - ansCalculates the degree of similarity between input records and
creates scorecard
Parser Transformation - ansFields with multiple information type and create new output
fields for each type
Standardizer Transformation - ansRemoves noise and creates standardized values
Weighted Average Transformation - ansReads scores generated by comparison and generates
weighted values based on inputs
Exception Transformation - ansUsed to create table required for exception and duplicate
management process
Distinct Values - ansUnique values within a column
Standardization - ansAddresses issues identified through profiling on correct completeness,
conformity, consistency, enhance and enrich information
Cleanse and Transform - ansRemoves values, Alters values and creates new values
Enrich - ansAppend bad data
Data Quality Precision - anssum of ports plus their join characters
Classifier - ansability to classify data based on classification model stored as part of a content
set
Classic Matching - ansSpecify what field you want to apply along with the algorithm.
Requires standardized inputs
Identity Matching - ansDelivers next generation linguistic and statistical matching algorithms
and enables business user to deliver accurate matches. Emulates a human experts ability to
determine a match and Delivers highest possible reliability
Threshold Value - ansPicks how exact or close the match of two rows will be up to 1 which is
a perfect match
Group Key - ansAllows records to be compared that only have the same group key,
ultimately reducing pairs and improving performance
,Data Quality Specialist
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Key Creation Types - ansString, Soundex, NYSIIS
Key Creation String - ansString first or last number of characters
Key Creation Soundex - ansGenerate alphanumeric code based on how a word sounds
Key Creation NYSIIS - ansConverts a word into its phonetic equivalent
Classic Match Strategies - ansEdit Distance, Jaro Distance, Bigram, Hamming Distance
Edit Distance - ansDerives match score for two values by calculating minimum cost of
transforming one string into another by insert, update, delete, replace
Jaro Distance - ansCalculates the similarity between two values if the first four characters are
the same. If the first four do not match score goes down by penalty property
Bigram - ansCalculates a match score by dividing the number of matched character pairs by
the total number of character pairs. Used for long text strings such as postal addresses
Hamming Distance - ansCalculates a match score by calculating the number of positions in
which characters differ between data strings. Use this when character position is a critical
factor such as telephone numbers, zip codes or product codes. Works well on strings of same
length.
Reverse Hamming - ansCalculates a match score by calculating the number of positions in
which characters differ between data strings reading from right to left. Use this when
character position is a critical factor such as telephone numbers, zip codes or product codes.
Works well on strings of same length.
Cluster ID - ansNumber that signifies records that match with each other will be assigned to
the same cluster
GroupKey - ansA key that is assigned to groups made from the string, soundex or NYSIIS
strategy. The field is the same Groupkey that was passed into the match transformation.
Cluster Size - ansNumber of records in a cluster, records that don't match with anything end
up in cluster sizes of 1
Row ID - ansVariation of Sequence ID passed into the Match Transformation
Driver ID - ansRecord in a cluster that is determined to be the driver or master record
Driver Score - ansScore calculated between a record and the Driver record
Link ID - ansIdentifies the record that pulled the current record in question into the cluster.
This does not have to be a master record
Link Score - ansScore between a record in question and the record that pulled the record in
question into the cluster
Match Cluster Analysis - ansShows statistics on the output from the match transformation,
consisting of Cluster ID, Groupkey, Clustersize, Row ID, Driver ID, Driver Score, Link ID,
Link Score
Workflow - ansGraphical representation defining a business process comprised of steps
Task - ansRuns unit of work in the workflow
Gateway - ansMakes decisions to split and merge paths in a workflow
Sequence flow - ansconnects workflow objects to specify the order that the Data Integration
Service (DIS) runs the objects
, Data Quality Specialist
Certification Exam Study Guide.
Latest Updated 2025/2026 with
100% Certified Questions and
Answers.
Human Task - ansContains steps that require human input to complete and can be used to fix
bad exceptions. Helps users participate in the business process
Assignment Task - ansValue to user defined workflow variable
Command Task - ansRuns single shell command, if successful returns 0 if not returns error
number
Mapping Task - ansRuns a mapping
Notification Task - ansSends out a notification or email to users
Task Performers - ansPeople who correct bad records
Token Parser - ansConsists of individual strings that match token sets or reference tables
Can a Token Parser standardize while parsing? - ansYes
Unparsed Output - ansNot matches found when parsing
Overflow - ansParsed but could not be send to an output port
Detailed Overflow - ansAllows users to see detail on which column overflowed
Token Parser Advantages - ansQuick to configure, Standardize as it parses, Multiple outputs
to the same output, Reverse parse, Append reference tables
Token Parser Disadvantages - ansNot sensitive, works better on unstructured data, output
types need to be well defined, Tries to use the first reference table, If a value is already parse
it overflows rather than parsing to subsequent tables
Pattern Parser - ansParses multiple strings to form patterns
What types of patterns can be used while parsing? - ansCustomer patterns, reference tables
and content set patterns
What is required when using a pattern parser? - ansThe input to the transformation must be
the output from the labeler transformation
Patterns are created in this transformation to be used in other transformations? - ansLabeler
How can you write patterns to a reference table or content set in order to be reused? -
ansRight click on a pattern and select send to reference table or new data domain
Pattern Parser Advantages - ansSpecify each pattern, Outputs can be determined on pattern
by basis, Multiple tokens output under same output column, Append reference tables and
content sets, Names of patterns that match content set patterns will auto parse
Pattern Parser Disadvantages - ansWorks better on structured data, Does not standardize as it
parses
Probabilistic Parsing Advantage - ansDoesn't need exact reference table matches since it can
make assumptions from a model
Model - ansSubset of read data where entity has been identified and provides a representation
for a data set used for training
How do you configure a probabilistic parser? - ansAdd a token parser, Add a strategy, Parse
tokens using probabilistic models, Rearrange ports and preview data, make required updates
Where can Analyst users review and update duplicate records? - ansAnalyst task inbox
The Analyst task inbox? - ansNotifies human tasks to users
