SAS Certified Data Quality Steward for SAS 9 Credential

SAS Certification Guide, A00-262,

Designed for individuals who are using DataFlux Data Management Studio to perform a variety of data quality tasks including profiling data, cleansing data and monitoring data for usability

Successful candidates should be able to

  • create and review data explorations and data profiles
  • create data jobs for data improvement
  • parameterize jobs and business rules within DataFlux Data Management Studio
  • create, maintain and apply business rules and tasks
  • understand the QKB components and various definition types
  • apply QKB components to address data quality issues
  • expand basic functionalities using Expression Engine Language (EEL)
  • use macro variables
  • create process jobs
  • configure the Data Management Server to run jobs.

SAS Data Quality Steward Exam A00-262 Requirement Details:

Required Exam to Appear for Data Quality Steward SAS Certification

Candidates who earn this credential will have earned a passing score on the SAS Data Quality using DataFlux Data Management Studio exam. This exam is administered by SAS and Pearson VUE.

  • 70 multiple-choice and short-answer questions (must achieve score of 68% correct to pass)
  • 110 minutes to complete exam
  • Use exam ID A00-262; required when registering with Pearson VUE.
  • This exam is based on SAS 9.4

SAS Data Quality Steward Programming Exam topics Details:

Navigating the DataFlux Data Management Studio Interface
Navigate within the Data Management Studio Interface
  • Register a new Quality Knowledge Base (QKB)
  • Create and connect to a repository
  • Define a data connection
  • Specify Data Management Studio options
  • Access the QKB
  • Create a name value macro pair
  • Access the business rules manager
  • Access the appropriate monitoring report
  • Attach and detach primary tabs
Exploring and Profiling data
Create, design and be able to explore data explorations and interpret results
Define and create data collections from exploration results
Create and explore a data profile
  • Create a data profile
    • Different sources: text file, filtered table, SQL query
  • Interpret the results
    • Frequency distribution
    • Pattern
  • Use collections from profile results
Design data standardization schemes
  • Build a scheme from profile results
  • Build a scheme manually
  • Update existing schemes
  • Import and export a scheme
Data Jobs
Create Data Jobs
  • Rename output fields
  • Add nodes and preview nodes
  • Run a data job
  • View a log and settings
  • Work with data job settings and data job displays
  • Best practices (ensure you are following a particular best practice such as
  • inserting notes, establishing naming conventions)
  • Work with branching
  • Join tables
  • Apply the Field layout node to control field order
  • Work with the Data Validation node:
    • Add it to the job flow
    • Specify properties/review properties
    • Edit settings for the Data Validation node
  • Work with data inputs
  • Work with data outputs
  • Profile data from within data jobs
  • Interact with the Repository from within Data Jobs
  • Debug levels for logging
Apply a Standardization definition and scheme.
  • Use a definition
  • Use a scheme
  • Determine the differences between definition and scheme
  • Explain what happens when you use both a definition and scheme
  • Review and interpret standardization results
  • Explain the different steps involved in the process of standardization
Apply Parsing definitions
  • Distinguish between different data types and their tokens
  • Review and interpret parsing results
  • Explain the different steps involved in the process of parsing
  • Use parsing definition
  • Interpret parse result codes
Apply Casing definitions
  • Describe casing methods: upper/lower/proper
  • Explain different techniques for accomplishing casing
  • Use casing definition
Compare and contrast the differences between identification analysis and right fielding nodes
  • Review results
  • Explain the technique used for identification (process of definition)
Apply the Gender Analysis node to determine gender
  • Use gender definition
  • Interpret results
  • Explain different techniques for conducting gender analysis
Create an Entity Resolution Job
  • Use a clustering node in a data job and explain its use
  • Survivorship (surviving record identification)
    • Record rules
    • Field rules
    • Options for survivorship
  • Discuss and apply the Cluster Diff node
  • Apply Cross-field matching
  • Entity resolution file output node
  • Use the Match Codes Node to select match definitions for selected fields.
    • Outline the various uses for match codes (join)
    • Use the definition
    • Interpret the results
    • Match versus match parsed
    • Explain the process for creating a match code
    • Select sensitivity for a selected match definition
    • Apply matching best practices
Use data job references within a data job
  • Use of external data provider node
  • Use of data job reference node
  • Define a target node
  • Explain why you would want to use a data job reference (best practice)
  • Real-time data service
Understand how to use an Extraction definition
  • Interpret the results
  • Explain the process of the definition
Explain the process of the definition of pattern analysis
Business Rules Monitoring
Define and create business rules
  • Use Business Rules Manager
  • Create a new business rule
    • Name/label rule
    • Specify type of rule
    • Define checks
    • Specify fields
  • Distinguish between different types of business rules
    • Row
    • Set
    • Group
  • Apply business rules
    • Profile
    • Execute business rule node
  • Use of Expression Builder
  • Apply best practices
Create new tasks
  • Understand events
    • Log error to repository
    • Set a data flow/key value
    • Log error to a text file
    • Write the row to a table
  • Applying tasks
    • Explain purpose of the data monitoring node
  • Review a data monitoring job log
  • Review a monitoring report
    • Trigger values
    • Filters
Data Management Server
Interact with the Data Management Server
  • Import/export jobs (special case profile)
  • Test service
  • Run history/job status
  • Identify the required configuration components (QKB, data, reference sources,
  • and repository)
  • Security, the access control list
  • Creation and use of WSDL
Expression Engine Language (EEL)
Explain the basic structure of EEL (components and syntax)
  • Identify basic structural components of the code
    • Statements
    • Functions
    • Declarations
  • Use EEL
    • Profile
    • Expression node (data job)
      • Tabs (expression, grouping, etc)
      • Order of Operations (pre/post, etc)
    • Expression node (process job)
    • Business rules
    • Custom metrics
      • Use in profile
      • Use in data job (execute custom metric node)
      • Use in business rule
    • Use in data validation node
Process Jobs
Work with and create process jobs
  • Add nodes and explain what nodes do
  • Interpret the log
  • Parameterizing process jobs
  • Identify Run options
  • Using different functionality in process jobs
  • If/then logic
    • Echo
    • Fork
    • Parallel iterator
    • Events and event handling (event listener)
    • Global get/set
    • Expression code features
      • Declaration of events
      • Set output slot
  • Embedded data job and data job reference
  • Using Work tables, process flow worktable reader
  • SAS code execution
  • SQL
Macro Variables and Advanced Properties and Settings
Work with and use macro variables in data profiles, data jobs and data monitoring
  • Define macro variables:
    • In DM studio
    • In Configuration files
    • With Command line
    • Dynamic
    • Use macro variables:
    • In a profile
    • In expression code
    • In a data job
    • In a process job
    • In business rules
  • Determine Scoping/precedence (order in which macros are read)
  • Compare/Contrast DM Studio versus DM Server
Determine uses for advanced properties
  • Multi-locale
    • Use locale guessing
    • Use with a scheme
    • Locale list and locale field
  • Apply setting for Max output rows
Quality Knowledge Base (QKB)
Describe the organization, structure and basic navigation of the QKB
  • Identify and describe locale levels (global, language, country)
  • Navigate the QKB (tab structure, copy definitions, etc)
  • Identify data types and tokens
Be able to articulate when to use the various components of the QKB. Components include:
  • Regular expressions
  • Schemes
  • Phonetics library
  • Vocabularies
  • Grammar
  • Chop Tables
Define the processing steps and components used in the different definition types.
  • Identify/describe the different definition types
    • Parsing
    • Standardization
    • Match
    • Identification
    • Casing
    • Extraction
    • Locale guess
    • Gender
    • Patterns
  • Explain the interaction between different definition types (with one another, parse within match, etc)

How to Prepare for SAS Data Quality Steward Certification?

Experience is a critical component to becoming a SAS Certified Professional. These resources are designed to help you prepare.

SAS Data Quality Steward Certification Online practice exams
SAS Data Quality Steward Certification Questions PDF for A00-262
SAS Data Quality Steward Certifications Questions and Answers