SAS A00-262 Certification Exam Syllabus

A00-262 Syllabus, A00-262 PDF Download, SAS A00-262 Dumps, SAS Data Quality Steward Dumps PDF Download, SAS Certified Data Quality Steward for SAS 9 PDF DownloadThis page is a one-stop solution for any information you may require for SAS Certified Data Quality Steward for SAS 9 (A00-262) Certification exam. The SAS A00-262 Exam Summary, Syllabus Topics and Sample Questions provide the base for the actual SAS Data Quality Using DataFlux Data Management Studio exam preparation, we have designed these resources to help you get ready to take your dream exam.

The SAS Certified Data Quality Steward for SAS 9 credential is globally recognized for validating SAS Data Quality Steward knowledge. With the SAS Data Quality Using DataFlux Data Management Studio Certification credential, you stand out in a crowd and prove that you have the SAS Data Quality Steward knowledge to make a difference within your organization. The SAS Certified Data Quality Steward for SAS 9 Certification (A00-262) exam will test the candidate's knowledge on following areas.

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 Using DataFlux® Data Management Studio
Understanding the SAS® Quality Knowledge Base
Creating a New Data Type in the Quality Knowledge Base
Books DataFlux Data Management Studio Documentation
DataFlux Data Management Server Documentation
Exam Registration Pearson VUE
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

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)

The SAS has created this credential to assess the knowledge and understanding of a candidate in the area as above via the certification exam. The SAS Data Quality Steward (A00-262) Certification exam contains a high value in the market being the brand value of the SAS attached to it. It is highly recommended to a candidate to do a thorough study and also get a hand full of the practice to clear SAS Certified Data Quality Steward for SAS 9 exam without any hiccups.

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