​What is SAS Text Analytics, Time Series, Experimentation and Optimization Certification?

​SAS Text Analytics, Time Series, Experimentation and Optimization  certification questions and exam summary helps you to get focused on the exam. This guide also helps you to be on A00-226 exam track to get certified with good score in the final exam.

It is essential that the candidate has a firm understanding and mastery of the functionalities for predictive modeling available in SAS 9.4. Successful candidates should have hands-on experience with a variety of SAS data preparation tools, including experience with the following analytical tools:
  • SAS Text Analytics
  • SAS/ETS
  • ​SAS/OR

A00-226 - SAS Text Analytics, Time Series, Experimentation and Optimization Certification Summary
A00-226 SAS Text Analytics, Time Series, Experimentation and Optimization Certification Questions:

Q 1: After creating a data source within the SAS Code node, which macro is used to modify the metadata of the data source (specifically changing the roles and levels for each variable)?

Options:
A: %EM_PROPERTY
B: %EM_REGISTER
C: %EM_METACHANGE
D: %EM_DECDATA

​Q 2: In the Text Topic node, the Singular Value Decomposition (SVD) dimensions are rotated. What is the purpose of this rotation?

Options:
A: To interpret each dimension with a set of terms.
B: To ensure the topics are relevant to your interests.
C: To determine the number of topics that are discovered.
​D: To avoid producing topics that are too similar

​Q 3: Refer to the exhibit below from an Incremental Response node from SAS Enterprise Miner.
What can be inferred from the properties above?

Options:
A: The input data set contains an expected revenue variable, with values for individual customers.
B: The expected revenue for individual customers is $9.50.
C: The expected revenue for individual customers is $10.
D: The expected revenue for individual customers is the estimated outcome from the model.

Q 4: What distinguishes a deterministic linear trend from other local linear trends?

Options:
A: A deterministic linear trend is always linear; other local linear trends are only linear over certain intervals.
B: A deterministic linear trend does not contain a seasonal component; other local linear trends do contain a seasonal component.
C: A deterministic linear trend shows the same slope at all time periods; other local linear trends do not show the same slope at all time periods.
D: A deterministic trend has a predetermined slope; other local linear trends do not have a predetermined slope.

Q 5: What is a primary value of text mining as applied to forensic linguistics analysis?

Options:
A: Determining the native language of a suspect can help identify where a suspect may reside.
B: The usage of certain emotion-based nouns, verbs, and adjectives indicate criminal pathology.
C: Word frequencies of written or spoken communication can help discriminate between suspects.
D: Determines if the written or spoken communication is the subject's second language.

Q 6: What is an example of time series forecasting?

Options:
A: A fire department wants to know how many fires it will likely need to fight during the holidays so that it can staff accordingly.
B: A dried fruit company sends out marketing postcards and models who will respond.
C: A hospital wants to know how long its patients will survive after open heart surgery so that adverse effects can be caught early.
D: A glue manufacturer wants to know how long it will take for its glue to dry.

​Answers:
Question: 1    Answer: C    
Question: 2    Answer: A
Question: 3    Answer: D    
​Question: 4    Answer: C
Question: 5    Answer: C    
Question: 6    Answer: A

SAS Text Analytics, Time Series, Experimentation and Optimization  Certification A00-226 Exam Syllabus:

1.Text Analytics (30%)
  • Create data sources for text mining
  • Import data into SAS Text Analytics
  • Use text mining to support forensic linguistics using stylometry techniques
  • Retrieve information for Analysis
  • Parse and quantify Text
  • Perform predictive modeling on text data
  • Use the High-Performance (HP) Text Miner Node

2. Time Series (30%)
  • Identify and define time series characteristics, components and the families of time series models
  • Diagnose, fit, and interpret ARIMAX Models
  • Diagnose, fit, and interpret Exponential Smoothing Models
  • Diagnose, fit, and interpret Unobserved Components Models

3. Experimentation & Incremental Response Models (20%)
  • Explain the role of experiments in answering business questions
  • Relate experimental design concepts and terminology to business concepts and terminology
  • Explain how incremental response models can identify cases that are most responsive to an action
  • Use the Incremental Response node in SAS Enterprise Miner

4. Optimization (20%)
  • Optimize linear programs
  • Optimize nonlinear programs
How to Register for SAS Text Analytics, Time Series, Experimentation and Optimization Certification Exam?
Exam Registration:
  • New User (Has never taken a SAS exam before):
    • First-time users must create a new web account within Pearson VUE before registering for a SAS exam.
    • It can take up to two business days to receive your username and password, which you will need for exam registration.
  • Returning Candidate (Has taken or registered to take a SAS exam(s) at Pearson VUE before BUT has never logged into SAS Certification Manager):
    • If you already have a Pearson VUE account but have forgotten your sign-in information, follow the links on the Pearson VUE site to retrieve this information.
  • Returning Candidate (Has taken SAS exam(s) and has logged in to SAS Certification Manager before):
    • Exam registrations must be completed at least 24 hours in advance and cannot be completed at the test facility.