A00-262: SAS Certified Data Quality Steward for SAS 9

Exam ID: A00-262

Exam Name: SAS Certified Data Quality Steward for SAS 9

Successful candidates should be able to:

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  • 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 DataFlux Data Management Server to run jobs.

SAS A00-262 Exam Summary:

Exam Name SAS Certified Data Quality Steward for SAS 9
Exam Code   A00-262
Exam Duration   110 minutes
Exam Questions   75 Multiple Choice Questions
Passing Score   68%
Exam Price   $180 (USD) 
Training DataFlux Data Management Studio: Essentials
DataFlux Data Management Studio: Advanced
DataFlux Data Management Studio: Customize the Quality Knowledge Base (QKB)
DataFlux Data Management Studio: Fast Track
DataFlux Data Management Studio: Customize the Quality Knowledge Base (QKB)
Books DataFlux Data Management Studio Documentation
DataFlux Data Management Server Documentation
Sample Questions   SAS Data Quality Steward Certification Sample Question
Practice Exam   SAS Data Quality Steward Certification Practice Exam

SAS A00-262 Exam Topics:

Objective 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 and explore a data profile – Create and explore a data profile
Different sources: text file, filtered table, SQL query
– Interpret the results
Frequency distribution
Pattern frequency distribution
Standard metrics
Visualizations
Alerts 
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
– Determine how data is processed
– Set sorting properties for the Data Sorting Node 
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)