MDM Saas Sample MCQ Library
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Questions and Answers

Which of the following accurately describes one of the consolidation states?

  • MATCH_INDEXED (correct)
  • MATCH_READY_FOR_VALIDATION
  • MATCH_PENDING
  • MATCH_IRRELEVANT

    • What does the base state NOT include?

  • Deleted
  • Active
  • Archived (correct)
  • Pending

    • Which of the following is a possible state in SearchIndex?

  • Search_cached
  • Search_dirty (correct)
  • Search_ready
  • Search_previous

    • Which consolidation state signifies that data has been successfully matched?

    <p>MATCHED</p> Signup and view all the answers

    What is NOT a type of record state mentioned?

    <p>Completed</p> Signup and view all the answers

    What is a significant advantage of using Informatica MDM in a SaaS model?

    <p>Enhanced scalability and flexibility</p> Signup and view all the answers

    Which feature of Informatica MDM allows for a comprehensive view of key entities?

    <p>360-Degree View</p> Signup and view all the answers

    What is a key implementation consideration when utilizing Informatica MDM?

    <p>Establishing data stewardship policies</p> Signup and view all the answers

    Which of the following best describes a benefit of using Informatica MDM's cloud-native design?

    <p>Optimal performance and resilience</p> Signup and view all the answers

    What challenge does Informatica MDM face regarding data management?

    <p>Continuous data quality maintenance</p> Signup and view all the answers

    In which area can Informatica MDM significantly enhance business operations?

    <p>Improving cross-departmental data collaboration</p> Signup and view all the answers

    What aspect of Informatica MDM architecture enables ease of updates and modular functionality?

    <p>Microservices Architecture</p> Signup and view all the answers

    What does high-quality data management in Informatica MDM assure?

    <p>Accurate and consistent master data</p> Signup and view all the answers

    What is the primary purpose of the match and merge processes in Master Data Management?

    <p>To enhance data quality by identifying duplicates</p> Signup and view all the answers

    Which method focuses on finding records that are identical in Master Data Management?

    <p>Exact Match</p> Signup and view all the answers

    What strategy retains the most recently updated record during the merging process?

    <p>Last Writer Wins</p> Signup and view all the answers

    Which of the following challenges is associated with the matching process in MDM?

    <p>Handling large volumes of data</p> Signup and view all the answers

    What is a benefit of implementing effective match and merge processes?

    <p>Streamlined operations and efficiency</p> Signup and view all the answers

    Which of the following best describes a recommended best practice in match and merge?

    <p>Establish clear rules for matching criteria</p> Signup and view all the answers

    What technological tool is used to analyze data quality before matching in MDM?

    <p>Data Profiling Tools</p> Signup and view all the answers

    What is one of the major complexities faced in the matching process?

    <p>Varied data formats and structures</p> Signup and view all the answers

    Which of the following is NOT a business entity that a car manufacturing company might define?

    <p>Machines</p> Signup and view all the answers

    A business entity consists solely of a single type of field and cannot have multiple fields.

    <p>False</p> Signup and view all the answers

    What is the significance of defining fields in a business entity?

    <p>Fields are defined based on the data that source systems will contribute to the master data.</p> Signup and view all the answers

    A company that manufactures cars might define business entities such as customers, employees, suppliers, factories, and __________.

    <p>products</p> Signup and view all the answers

    Match the following terms with their definitions:

    <p>Business entity = An entity of importance to an organization consisting of fields. Fields = Components that support the data needs within a business entity. Master data = The critical data that is essential for business operations. Source systems = Systems that contribute data to master data management.</p> Signup and view all the answers

    A crosswalk represents a two-way relationship between code values in a pair of code lists.

    <p>False</p> Signup and view all the answers

    A reference data set can contain multiple code lists with various code values.

    <p>True</p> Signup and view all the answers

    Crosswalks provide a way to translate between identical code lists only.

    <p>False</p> Signup and view all the answers

    Each code list in a reference data set can contain different types of code values.

    <p>False</p> Signup and view all the answers

    Crosswalks are essential for effectively managing relationships between varied code values in reference data.

    <p>True</p> Signup and view all the answers

    What is the primary function of a crosswalk in code lists?

    <p>To visually represent relationships between code values in different code lists</p> Signup and view all the answers

    How does a reference data set relate to code lists?

    <p>It comprises several code lists that reflect variations of the same type of code values</p> Signup and view all the answers

    What is a major benefit of using crosswalks in data management?

    <p>They enable translation between different variations of code values used in code lists</p> Signup and view all the answers

    Which statement about code lists in a reference data set is accurate?

    <p>Reference data sets may contain multiple code lists with variations of the same code values</p> Signup and view all the answers

    What type of relationship does a crosswalk illustrate?

    <p>A one-way relationship between code values in separate code lists</p> Signup and view all the answers

    What is required to use the DaaS services effectively?

    <p>Valid license keys in the Global Settings page</p> Signup and view all the answers

    Which DaaS rule association is used for real-time phone number verification?

    <p>Informatica Global Phone Validation</p> Signup and view all the answers

    How does the predefined address verifier asset contribute to batch processing?

    <p>It contains a mapping of input and output fields for address validation.</p> Signup and view all the answers

    What can DaaS rule associations be used for in terms of email addresses?

    <p>Real-time email verification</p> Signup and view all the answers

    What distinguishes the DaaS rule association for real-time address validation from the batch processing method?

    <p>The technology provider utilized</p> Signup and view all the answers

    What defines a basic rule association?

    <p>An association between a business entity field and a simple condition-based rule.</p> Signup and view all the answers

    Which of the following functions can you configure for basic rule associations?

    <p>Concatenate first and last names to generate a full name.</p> Signup and view all the answers

    What is a defining characteristic of advanced rule associations?

    <p>They involve mapping input and output fields of a rule specification.</p> Signup and view all the answers

    Which rule specification is supported for advanced rule associations?

    <p>DUNS and SSN Validation</p> Signup and view all the answers

    What can DaaS rule associations be applied to?

    <p>Fields that store postal addresses, emails, and phone numbers.</p> Signup and view all the answers

    Which condition can basic rule associations NOT validate?

    <p>Required fields based on defined rules.</p> Signup and view all the answers

    Which function would NOT be typically performed by an advanced rule association?

    <p>Transforming data from one format to another.</p> Signup and view all the answers

    What is one primary use of basic rule associations?

    <p>To perform high concurrent cleansing transactions.</p> Signup and view all the answers

    A DaaS rule association can be used to validate email addresses in batches.

    <p>False</p> Signup and view all the answers

    To validate addresses in bulk, a predefined address verifier asset is utilized from Cloud Data Quality.

    <p>True</p> Signup and view all the answers

    Informatica Global Phone Validation is used to validate email addresses in real time.

    <p>False</p> Signup and view all the answers

    Business 360 Console defaults to Informatica Email Verification for real-time email validation.

    <p>False</p> Signup and view all the answers

    Custom field groups can be validated and enriched in batches using a DaaS rule association.

    <p>False</p> Signup and view all the answers

    A basic rule association can validate input fields that are required.

    <p>False</p> Signup and view all the answers

    Advanced rule associations require the mapping of both input and output fields to the business entity fields.

    <p>True</p> Signup and view all the answers

    DaaS rule associations can only be applied to fields that contain names and addresses.

    <p>False</p> Signup and view all the answers

    A transformation function in a basic rule association alters the source data.

    <p>True</p> Signup and view all the answers

    The DUNS SSN Validation rule is an example of a predefined rule specification for advanced rule associations.

    <p>True</p> Signup and view all the answers

    Basic rule associations can be used for high concurrent cleansing transactions.

    <p>True</p> Signup and view all the answers

    Integrity checks are considered a type of rule association mentioned in the content.

    <p>True</p> Signup and view all the answers

    Input fields that are empty are always validated by advanced rule associations.

    <p>False</p> Signup and view all the answers

    What is the primary purpose of the survivorship process in data management?

    <p>To create a master record from multiple matching source records.</p> Signup and view all the answers

    How are source systems ranked during the survivorship configuration?

    <p>According to the accuracy and credibility of data they provide.</p> Signup and view all the answers

    What happens to the ranking label when modifications are made to source system rankings?

    <p>A new label is assigned incrementally.</p> Signup and view all the answers

    Which type of survivorship rule identifies trusted values based on decay rates and trust levels?

    <p>Decay Rule</p> Signup and view all the answers

    What does a higher rank indicate in source system reliability?

    <p>The data is more reliable.</p> Signup and view all the answers

    In survivorship configuration, what defines the conditions for fields and field groups to survive?

    <p>Survivorship Rules</p> Signup and view all the answers

    Which of the following best describes a modification in survivorship rules?

    <p>Modifications may involve adding, editing, or deleting rules.</p> Signup and view all the answers

    What does the term 'decay rate' in survivorship rules refer to?

    <p>The timeframe within which trust levels diminish.</p> Signup and view all the answers

    What purpose does configuring a decay rule for a field serve?

    <p>To identify a source system with a higher trust level</p> Signup and view all the answers

    What is the function of the Source Last Updated Date field in the survivorship process?

    <p>It determines the survivorship when trust scores are identical</p> Signup and view all the answers

    What happens if block survivorship is enabled for a field group?

    <p>All fields within the group share the same survivorship configuration</p> Signup and view all the answers

    How does deduplication criteria function within a field group?

    <p>It identifies duplicates based on specific field values</p> Signup and view all the answers

    When should a minimum rule be applied to a field value during survivorship?

    <p>When the value with the earliest timestamp should survive</p> Signup and view all the answers

    What configuration must be set to enable fields and field groups to survive as a single unit?

    <p>Block survivorship configuration</p> Signup and view all the answers

    What is the outcome if multiple fields have identical field values during survivorship evaluation?

    <p>MDM cannot determine the value to survive</p> Signup and view all the answers

    What does configuring a maximum rule for a field help achieve?

    <p>To survive the highest value based on the latest timestamp</p> Signup and view all the answers

    In the case where source systems have equal rankings, what decides which field value survives?

    <p>Source last updated date</p> Signup and view all the answers

    What would happen if the Source Last Updated Date field is disabled?

    <p>Trust score is the only determinant</p> Signup and view all the answers

    What defines the winner in a survivorship configuration when two records have identical availability dates?

    <p>The next field in the comparison</p> Signup and view all the answers

    What happens if both records have the same last updated date during survivorship evaluation?

    <p>MDM cannot determine which value to trust</p> Signup and view all the answers

    How can the trust score of source systems impact the data survivorship process?

    <p>Higher trust scores from source systems lead to greater reliability</p> Signup and view all the answers

    What is the role of deduplication criteria when applied to mandatory fields?

    <p>They enable identification of duplicates based on specific field value comparisons</p> Signup and view all the answers

    An exact match strategy is designed to identify similar records.

    <p>False</p> Signup and view all the answers

    A predefined match model can be edited to suit specific business needs.

    <p>False</p> Signup and view all the answers

    Adding at least one exact match rule in a match model helps reduce overmatching.

    <p>True</p> Signup and view all the answers

    Machine learning (ML) models do not require any training processes.

    <p>False</p> Signup and view all the answers

    Custom match models can be created from scratch or copied from existing models.

    <p>True</p> Signup and view all the answers

    Study Notes

    Record States

    • A record can be in one of three states: Active, Pending, or Deleted.

    Consolidation States

    • Various consolidation states represent the progress of record matching and consolidation.
    • MATCH_DIRTY: Indicates records are awaiting matching criteria checks.
    • MATCH_INDEXED: Records have been indexed for matching.
    • MATCHED: Records have been successfully matched against other records.
    • CONSOLIDATED: Records have been consolidated into a single representative record.
    • NOT_READY_FOR_MATCH: Records are not yet ready for matching due to missing or incomplete data.

    SearchIndex States

    • SearchIndex is a component used for efficient record matching.
    • Search_dirty: Indicates the SearchIndex needs to be updated.
    • Search_indexed: The SearchIndex is up-to-date and ready for matching.

    CreateMode

    • A state that determines how new records are handled during data consolidation.

    Validation

    • A process for verifying the accuracy and completeness of data in an Informatica environment.

    Informatica MDM SaaS Overview

    • A cloud-based solution that focuses on ensuring consistent and accurate master data across an organization.
    • Offers scalability, flexibility, and reduced on-premises IT costs through a Software as a Service (SaaS) model.

    ### Key Features

    • Integrates seamlessly with various data sources and applications.
    • Ensures high-quality, consistent data through cleansing and validation processes.
    • Facilitates management of workflows and business rules for master data.
    • Provides a comprehensive view of customers, products, and other key entities.
    • Manages multiple domains (e.g., customers, suppliers, products) within a single platform.

    Benefits

    • Easily adapts to growing data needs without significant infrastructure changes.
    • Reduces overhead costs related to hardware and maintenance.
    • Rapidly deploys master data management capabilities.
    • Enables cross-departmental data collaboration for enhanced decision-making.

    Architecture

    • Designed for optimal performance and resilience in a cloud environment.
    • Utilizes microservices architecture for modular functionality and ease of updates.
    • Built-in security measures to comply with industry regulations and protect sensitive data.

    Use Cases

    • Enhances customer engagement and personalized service through Customer 360 initiatives.
    • Maintains compliance with data governance standards for regulatory purposes.
    • Ensures accurate and consistent supplier and product data for supply chain management.

    Implementation Considerations

    • Establish clear policies for data stewardship and governance.
    • Plan for stakeholder engagement and training during implementation.
    • Identify critical integrations with existing systems early in the process.

    Challenges

    • Overcome issues related to fragmented data storage across various systems.
    • Ensure that end-users are adequately trained and supportive of the MDM processes.
    • Maintain ongoing data quality efforts post-implementation.

    Master Data Management (MDM)

    • Centralizes and manages key business data like customers, products, and suppliers.
    • Aims to ensure a single, consistent view of data across an organization.

    Match and Merge in MDM

    • Processes used to identify and consolidate duplicate records.

    Matching

    • Goal: Identify records that refer to the same real-world entity.
    • Techniques:
      • Exact Match: Identifies identical records.
      • Fuzzy Match: Identifies similar records based on defined criteria, accommodating misspellings or variations.
    • Matching Criteria:
      • Name, address, phone number, email, etc.
    • Methods:
      • Rule-based matching: Uses pre-defined rules to determine matches.
      • Probabilistic matching: Uses statistical algorithms to assess match probability.

    Merging

    • Goal: Combine duplicate records into one single, accurate record.
    • Merge Strategies:
      • Last Writer Wins: The most recently updated record is retained.
      • Field Prioritization: Certain fields are prioritized over others.

    Benefits of Match and Merge

    • Improved Data Quality: Ensures accuracy, consistency, and reliability of data across the organization.
    • Operational Efficiency: Eliminates redundant data, streamlining operations.
    • Informed Decision Making: Provides a single source of truth for analysis and reporting.

    Challenges of Match and Merge

    • Data Complexity: Variations in data formats and structures make matching difficult.
    • Scalability: Managing large volumes of data can strain matching algorithms.
    • False Positives and Negatives: Mistakes in matching can lead to data inaccuracies.

    Best Practices for Match and Merge

    • Establish Clear Rules: Define criteria for matching and rules for merging.
    • Iterative Process: Continuously refine matching algorithms based on feedback and results.
    • Data Stewardship: Involve human oversight in verifying matches and merges.
    • Monitoring and Auditing: Track match/merge activities and outcomes to ensure quality.

    Technologies used in Match and Merge

    • Data Profiling Tools: Analyze data quality and prepare it for matching.
    • Machine Learning Algorithms: Enhance matching accuracy over time through learning.
    • Integration with Other Systems: Connect MDM with CRM, ERP, and other data sources for comprehensive data management.

    Business Entities

    • A business entity is an essential component of an organization's data structure.
    • Entities represent real-world objects relevant to the business, like customers, employees, or products.
    • The fields within an entity define the data points needed to effectively manage and analyze information.
    • Business entities are designed to integrate data from different source systems into a unified master data repository.
    • For example, a car manufacturing company might use entities like customers, employees, suppliers, factories, materials, and products to organize its data.
    • The specific entities and fields used will depend on the organization's data needs and the information provided by its source systems.
    • Once defined, business entities can be configured and customized to include the required fields for data management.

    Crosswalks

    • A crosswalk is a visual representation of a one-way relationship between code values in a pair of code lists
    • Crosswalks enable translation between different variations of the same type of code value within a reference data set
    • Reference data sets often contain many code lists, each of which represents a variation of the same type of code value

    Crosswalks in Reference Data Sets

    • A crosswalk is a visual representation of a one-way relationship between code values.
    • This relationship exists between a pair of code lists.
    • A reference data set can contain multiple code lists.
    • Each code list in a reference data set contains a variation of the same type of code values.
    • Crosswalks enable translation between these variations in different code lists.

    Rule Associations for Business Entities

    • Basic Rule Associations

      • Link a business entity field to a simple rule.
      • Conditions use predefined functions like "Regular Match" and "Concatenate".
      • Best for high volume cleansing transactions.
      • Example: combining first and last names into a full name field.
      • Used for input validation and transformation.

    • Advanced Rule Associations

      • Connect a business entity field to a Cloud Data Quality rule specification.
      • Use predefined or customized rules from Cloud Data Quality.
      • Map input and output fields of the rule with entity fields.
      • Example: DUNS SSN Validation for verifying numbers.
      • Validate empty fields with a returned value.

    • DaaS Rule Associations

      • Link a field group to Informatica Data as a Service (DaaS).
      • Only applicable to postal addresses, email addresses, and phone numbers.
      • Requires valid license keys in Global Settings.
      • Used for batch and real-time validation and enrichment.

    DaaS Rule Association Types

    • Batch Processing

      • Uses a predefined address verifier asset from Cloud Data Quality.
      • Predefined mapping of input and output fields.
      • No support for email, phone, or custom field groups in batches.

    • Real-Time Processing

      • Uses Informatica Address Verification as the default DaaS provider.
      • Predefined mapping of address verification fields.
      • Supports real-time enrichment of email addresses and phone numbers.
      • Can create custom DaaS rules for field groups with custom mappings.

    Rule Associations in Business Entity Fields

    • Basic rule associations are simple conditions-based rules linked to a business entity field.

      • Conditions are predefined functions like Regular Match and Concatenate.
      • Use Cases:
        • High concurrent transactions for data cleansing, e.g., merging first and last names for full name generation.
        • Validating empty input fields.
      • Not applicable for required fields.
      • Transformation or validation functions can be used to set conditions:
        • Validation functions check if data matches the specified condition.
        • Transformation functions modify source data.

    • Advanced rule associations link business entity fields to Cloud Data Quality rule specifications.

      • Predefined rule specifications can be used, or custom specifications can be created within Cloud Data Quality.
      • Mapping Input/Output: Input and output fields of the rule specification must be mapped to business entity fields.
      • Use Cases:
        • Validating empty input fields.
        • Validate specific formats like DUNS and Social Security numbers using the "DUNS SSN Validation" rule specification.

    • DaaS (Data as a Service) rule associations link field groups to DaaS services.

      • Applicable Fields: Postal addresses, email addresses, and phone numbers.
      • License Requirements: Valid license keys must be added in Global Settings.
      • Use Cases:
        • Batch processing: Use a predefined address verifier asset from Cloud Data Quality to validate and enrich addresses in bulk.
        • Real-time processing:
          • Addresses: Use Informatica Address Verification.
          • Email addresses: Use Informatica Email Verification.
          • Phone numbers: Use Informatica Global Phone Validation.

    DaaS Rule Association Details

    • Batch processing:

      • Uses the predefined address verifier asset from Cloud Data Quality.
      • This asset has pre-defined field mappings.
      • Not applicable for validating/enriching email, phone, or custom field groups in batches.

    • Real-time processing:

      • Business 360 Console uses Informatica Address Verification as the default DaaS provider for real-time address validation.
      • Predefined field groups come with a default DaaS rule association and pre-mapped fields from the address verification DaaS provider to business entity fields.
      • Custom DaaS rules: Can be created for custom field groups and mapped as needed.

    Survivorship Process

    • After matching source records, the Survivorship process creates a master record with the most trusted values
    • Requires configuring survivorship rules and ranking source systems

    Source Ranking

    • Determines the reliability of source systems
    • Higher rank = more reliable data
    • Each ranking is saved with a unique label (e.g., Rank 1, Rank 2 etc)

    Survivorship Rules

    • Define conditions to determine the trusted value for a field
    • Can be applied to individual fields, field groups, or all fields in an entity
    • Types include Decay, Maximum, Minimum, Source Last Updated, and Block Survivorship

    Decay Survivorship

    • Trusted value based on trust levels and decay rate of field values
    • Trust level: confidence in the source system
    • Decay rate: rate at which trust level decreases over time
    • Uses a picklist to determine the code value for survivorship

    Maximum Survivorship

    • Identifies the maximum value of a field as the trusted value
    • Uses a picklist to determine the code value for survivorship

    Minimum Survivorship

    • Identifies the minimum value of a field as the trusted value

    Source Last Updated Survivorship

    • Helps determine the trusted value during the merge process
    • When trust score and source ranking are equal, the Source Last Updated Date field determines the trusted value
    • Can be disabled, in which case trust score, source ranking, and last updated date determine survivorship

    Block Survivorship

    • Treats a field group as a single unit, applying survivorship configuration to all fields within the block
    • By default, field groups are treated as blocks
    • Can be disabled, allowing for specific configurations for fields within the group
    • Nested field groups can be disabled from block survivorship as well

    Deduplication Criteria

    • Used to identify and merge duplicate field group values
    • Applies survivorship rules to determine which values survive
    • Can be configured for mandatory, nested, and optional fields
    • Duplication identification is case-insensitive

    Survivorship Configuration Summary

    • Survivorship rules: Determine trusted value based on field value
    • Source system ranking: Field values from higher ranked systems are trusted
    • Source last updated date: Most recent updated record wins
    • Last updated record in MDM SaaS: Most recently updated record in MDM SaaS wins
    • Latest created record in MDM SaaS: Most recently created record in MDM SaaS wins

    Match and Merge Process

    • The match and merge process is configured to identify and resolve duplicate records.
    • This configuration, known as the match model, uses either machine learning (ML) models, declarative match rules, or both.
    • Declarative match rules employ exact or fuzzy match strategies for identifying similar records.
    • To ensure optimal performance, including at least one exact match rule in the match model is recommended.
    • ML models require a training process based on user-labeled record pairs, which minimizes the need for extensive declarative match rules.
    • Users can configure custom match models by creating them from scratch or copying existing models, but predefined models cannot be edited.
    • Declarative match rules define conditions and business entity fields to detect duplicates.
    • Configurable properties within declarative match rules include:
      • Description
      • Match strategy
      • Merge strategy
      • Match criteria
      • Match level
      • Merge threshold

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