• Module 1: Describe deployment of SAS Data Integration Studio jobs as a SAS Stored Process/ Define the change management feature of SAS Data Integration Studio
• Module 2: Discuss various types of keys and how to define in SAS Data Integration Studio/ Register metadata for a comma-delimited external file
• Module 3: Define administrative tasks to be performed for SAS Data Integration Studio/ Define the architecture of the platform for SAS Business Analytics
• Module 4: Discuss and use transformations in the SQL grouping of transformations/ Use Register Tables wizard to register source data
• Module 5: Discuss and use the Apply Lookup Standardization transformation/ Discuss the DataFlux Data Management Server
• Module 6: Discuss the Schedule Manager in SAS Management Console/ Creating Metadata for Source and Target Data
• Module 7: Discuss and use the Extract and Summary Statistics transformation/ Discuss indexes and how to define in SAS Data Integration Studio
• Module 8: Discuss components of Join’s Designer window/ Describe the available interfaces
• Module 9: Discuss the types of job deployment available for SAS Data Integration Studio Jobs/ Creating Metadata for Target Data and Jobs
• Module 10: Explain the functionality of the Data Validation transformation/ Describe features of the New Table wizard
• Module 11: Discuss and use the Rank, Transpose, Append, List and Sort transformations/ Apply and use the Standardize with Definition transformation
• Module 12: Define SAS code transformation templates/ Describe the New Library Wizard
• Module 13: Define Impact and Reverse Impact Analysis/ Generate reports on metadata for tables and jobs

Expected A00-240 Exam Topics, as suggested by SAS :

• Module 1: Identify the potential challenges when preparing input data for a model/ Detect and analyze interactions between factors
• Module 2: Model selection and validation using training and validation data/ Perform ANOVA post hoc test to evaluate treatment effect
• Module 3: Establish effective decision cut-off values for scoring/ Fit a multiple linear regression model using the REG and GLM procedures
• Module 4: Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection/ Use the REG or GLMSELECT procedure to perform model selection
• Module 5: Screen variables for irrelevance and non-linear association using the CORR procedure/ Perform logistic regression with the LOGISTIC procedure
• Module 6: Screen variables for non-linearity using empirical logit plots/ Score new data sets using the LOGISTIC and PLM procedures
• Module 7: Apply the principles of honest assessment to model performance measurement/ Optimize model performance through input selection
• Module 8: Analyze differences between population means using the GLM and TTEST procedures/ Assess classifier performance using the confusion matrix
• Module 9: Assess the validity of a given regression model through the use of diagnostic and residual analysis/ Prepare Inputs for Predictive Model Performance
• Module 10: Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models/ Improve the predictive power of categorical inputs

• Module 1: Access and Create Data Structures: This section of the exam measures skills of Data Analysts and covers the creation of temporary and permanent SAS data sets using the DATA step. Candidates are expected to investigate SAS data libraries using utility procedures such as LIBNAME and PROC CONTENTS. They must demonstrate proficiency in accessing data through the SET statement and PROC IMPORT, including reading delimited and Microsoft Excel files. Additionally, they must understand data set combination techniques such as concatenation and merging, manage SAS date values using appropriate informats and formats, and apply filtering techniques using WHERE, IF, DROP, and KEEP statements.
• Module 2: Manage Data: This section of the exam measures skills of Data Engineers and covers essential data management techniques in SAS. Candidates must be able to sort observations using the SORT procedure, eliminate duplicates, and conditionally execute statements using IF-THEN/ELSE and DO-END structures. They should be proficient in creating and modifying variables using assignment statements and formatting options, such as LABEL and FORMAT. The section also covers methods for accumulating subtotals using BY-group processing and RETAIN and SUM statements. Additionally, candidates must manipulate character and numeric data using SAS functions such as SCAN, SUBSTR, ROUND, and INT, perform type conversions with INPUT and PUT functions, and process data iteratively using DO loops.
• Module 3: Error Handling: This section of the exam measures skills of SAS Programmers and covers techniques for identifying and resolving programming errors. Candidates must be able to debug logic errors using the PUTLOG statement and temporary variables like N and ERROR. The section emphasizes recognizing and correcting syntax errors, including misspelled keywords, missing semicolons, and unmatched quotation marks. Candidates should also know how to interpret SAS logs to diagnose issues effectively and apply best practices to prevent common errors.
• Module 4: Generate Reports and Output: This section of the exam measures skills of Business Intelligence Analysts and covers generating structured reports using SAS procedures. Candidates must use PROC PRINT to create list reports while modifying output through statements like VAR, SUM, and WHERE. The section also focuses on generating summary reports using PROC FREQ, MEANS, and UNIVARIATE to analyze data distribution and validate information. Enhancing reports through user-defined formats, titles, and footnotes is also covered. Candidates are expected to utilize the Output Delivery System (ODS) to create reports in multiple formats such as HTML, PDF, RTF, XLSX, and CSV. Lastly, exporting data using PROC EXPORT and the SAS/ACCESS XLSX engine is an essential competency.