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Question 1 of 10
1. Question
A new business initiative at a fintech lender requires guidance on SAS Macro for Business Model Canvas Management Management Management Management Analytics as part of control testing. The proposal raises questions about how to efficiently process customer transaction strings to identify specific risk patterns within a 30-day lookback period. The internal audit team is reviewing a SAS program that uses the SCAN function to extract the third word from a character variable named ‘Transaction_Note’, which uses a tilde (~) as a delimiter. To ensure the integrity of the risk assessment data, the auditor must confirm how the system handles records where the ‘Transaction_Note’ contains fewer than three segments. Which behavior of the SCAN function should the auditor verify to ensure the data step continues processing without error while identifying missing segments?
Correct
Correct: In SAS programming, the SCAN function is used to extract words from a character string. If the count argument (the position of the word to be extracted) is greater than the number of words available in the string, the function returns a blank value. This is a critical feature for auditors to understand because it allows the program to continue running without interruption, even if the data format is inconsistent, though it necessitates a control check for blank values in the output.
Incorrect: The SCAN function does not generate a runtime error or change the _ERROR_ variable when a word position is not found; it simply returns a blank. It does not return the entire original string or default to the last available word; these behaviors would lead to data inaccuracies and are not part of the standard SAS SCAN function logic. Understanding these distinctions is vital for an auditor to ensure that data extraction logic does not produce misleading results.
Takeaway: The SCAN function returns a blank value when the requested word position exceeds the number of words present in the target string, allowing for error-free processing of inconsistent data lengths.
Incorrect
Correct: In SAS programming, the SCAN function is used to extract words from a character string. If the count argument (the position of the word to be extracted) is greater than the number of words available in the string, the function returns a blank value. This is a critical feature for auditors to understand because it allows the program to continue running without interruption, even if the data format is inconsistent, though it necessitates a control check for blank values in the output.
Incorrect: The SCAN function does not generate a runtime error or change the _ERROR_ variable when a word position is not found; it simply returns a blank. It does not return the entire original string or default to the last available word; these behaviors would lead to data inaccuracies and are not part of the standard SAS SCAN function logic. Understanding these distinctions is vital for an auditor to ensure that data extraction logic does not produce misleading results.
Takeaway: The SCAN function returns a blank value when the requested word position exceeds the number of words present in the target string, allowing for error-free processing of inconsistent data lengths.
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Question 2 of 10
2. Question
An incident ticket at an investment firm is raised about SAS Macro for Cultural Management Management Analytics during onboarding. The report states that the automated data validation process is failing to properly sanitize the ‘Global_Region_Code’ variable. The internal audit team found that while the COMPRESS function is currently used to remove blanks, it is failing to remove various punctuation marks that are causing downstream integration errors in the firm’s reporting system. To comply with the data governance policy, the script must be updated to remove all punctuation characters efficiently. Which modification to the SAS programming logic best addresses this requirement?
Correct
Correct: The COMPRESS function in SAS is designed to remove specific characters from a string. By using the ‘p’ modifier as the third argument, the function is instructed to remove all punctuation characters automatically. This is the most efficient and comprehensive solution for data sanitization in an automated environment, addressing the audit finding regarding data integrity and downstream system errors.
Incorrect
Correct: The COMPRESS function in SAS is designed to remove specific characters from a string. By using the ‘p’ modifier as the third argument, the function is instructed to remove all punctuation characters automatically. This is the most efficient and comprehensive solution for data sanitization in an automated environment, addressing the audit finding regarding data integrity and downstream system errors.
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Question 3 of 10
3. Question
As the information security manager at a private bank, you are reviewing SAS Macro for Problem Management Management Management Analytics during model risk when a regulator information request arrives on your desk. It reveals that the data ingestion process for incident logs may be misinterpreting records where the final character variable is shorter than the defined length in the input buffer. To ensure the integrity of the problem management analytics, you must verify the INFILE statement options used to read these external text files. Which option should be used to ensure that SAS assigns the available characters to the variable when a record is shorter than the defined length, rather than skipping the value or reading from the next record?
Correct
Correct: The TRUNCOVER option is the correct choice because it instructs SAS to use the available data in the current record to populate the variable, even if the record ends before the specified length is reached. This prevents SAS from moving to the next line to find data (the default FLOWOVER behavior) and ensures that partial data is captured rather than being set to missing (the MISSOVER behavior).
Incorrect
Correct: The TRUNCOVER option is the correct choice because it instructs SAS to use the available data in the current record to populate the variable, even if the record ends before the specified length is reached. This prevents SAS from moving to the next line to find data (the default FLOWOVER behavior) and ensures that partial data is captured rather than being set to missing (the MISSOVER behavior).
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Question 4 of 10
4. Question
A regulatory guidance update affects how a credit union must handle SAS Macro for Continuity Management Management Management Analytics in the context of transaction monitoring. The new requirement implies that internal auditors must validate the logic within a macro that parses legacy transaction strings to ensure data integrity. The macro is required to extract a 4-digit branch code located at the very end of a variable-length ‘Description’ field, which is the third item in a tilde-delimited (~) raw data record. Which SAS programming approach ensures the branch code is accurately captured for every observation regardless of the description’s length?
Correct
Correct: The SCAN function is the most efficient way to extract a specific segment from a delimited string, such as the third field in a tilde-separated record. Because the ‘Description’ field has a variable length, the LENGTH function must be used to calculate the total number of characters in that specific segment. By using the result of the LENGTH function minus three as the starting position for the SUBSTR function, the program can dynamically and accurately extract the final four characters (the branch code) for every record, meeting the regulatory requirement for data precision.
Incorrect: Using a fixed starting position with the SUBSTR function is inappropriate for delimited data where preceding fields vary in length, as it would lead to misaligned data extraction. The FIND function can locate characters but is less efficient than SCAN for isolating specific fields in a delimited list and does not inherently handle the extraction of the last four characters. The TRANWRD function is used for string replacement and is redundant here because the SCAN function already removes the delimiters from the isolated field; furthermore, TRANWRD does not assist in identifying the end of a variable-length string.
Takeaway: To accurately parse variable-length delimited data, use the SCAN function for field isolation and combine SUBSTR with LENGTH for positional extraction from the end of a string.
Incorrect
Correct: The SCAN function is the most efficient way to extract a specific segment from a delimited string, such as the third field in a tilde-separated record. Because the ‘Description’ field has a variable length, the LENGTH function must be used to calculate the total number of characters in that specific segment. By using the result of the LENGTH function minus three as the starting position for the SUBSTR function, the program can dynamically and accurately extract the final four characters (the branch code) for every record, meeting the regulatory requirement for data precision.
Incorrect: Using a fixed starting position with the SUBSTR function is inappropriate for delimited data where preceding fields vary in length, as it would lead to misaligned data extraction. The FIND function can locate characters but is less efficient than SCAN for isolating specific fields in a delimited list and does not inherently handle the extraction of the last four characters. The TRANWRD function is used for string replacement and is redundant here because the SCAN function already removes the delimiters from the isolated field; furthermore, TRANWRD does not assist in identifying the end of a variable-length string.
Takeaway: To accurately parse variable-length delimited data, use the SCAN function for field isolation and combine SUBSTR with LENGTH for positional extraction from the end of a string.
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Question 5 of 10
5. Question
The risk committee at a wealth manager is debating standards for SAS Macro for Issue Management Management Management Management Management Management Analytics as part of client suitability. The central issue is that the current macro logic used to parse client disclosure statements frequently fails when the sequence of information changes. An internal audit of the system’s 180-day performance revealed that the code relies on fixed-position character extraction, which results in false negatives for high-risk clients when their disclosures are not formatted identically. To mitigate this operational risk and ensure the macro can dynamically identify specific risk keywords separated by varying delimiters, which SAS function should be implemented?
Correct
Correct: The SCAN function is the most appropriate choice because it is designed to extract words or tokens from a character string based on specified delimiters (such as spaces, commas, or semicolons). Unlike position-based functions, SCAN does not require the target text to be in a specific location within the string, making it highly effective for parsing unstructured or semi-structured client disclosure data where the order of information may vary.
Incorrect: SUBSTR is incorrect because it requires a fixed starting position and a specific length, which leads to errors when the data format is inconsistent. VERIFY is incorrect because it is used to find the first character in a string that does not appear in a search string, typically for data validation rather than extraction. COMPRESS is incorrect because its primary purpose is to remove specific characters or whitespace from a string, which does not help in isolating or extracting specific risk indicators.
Takeaway: Using delimiter-based functions like SCAN instead of position-based functions like SUBSTR increases the reliability of automated risk flagging in unstructured data environments.
Incorrect
Correct: The SCAN function is the most appropriate choice because it is designed to extract words or tokens from a character string based on specified delimiters (such as spaces, commas, or semicolons). Unlike position-based functions, SCAN does not require the target text to be in a specific location within the string, making it highly effective for parsing unstructured or semi-structured client disclosure data where the order of information may vary.
Incorrect: SUBSTR is incorrect because it requires a fixed starting position and a specific length, which leads to errors when the data format is inconsistent. VERIFY is incorrect because it is used to find the first character in a string that does not appear in a search string, typically for data validation rather than extraction. COMPRESS is incorrect because its primary purpose is to remove specific characters or whitespace from a string, which does not help in isolating or extracting specific risk indicators.
Takeaway: Using delimiter-based functions like SCAN instead of position-based functions like SUBSTR increases the reliability of automated risk flagging in unstructured data environments.
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Question 6 of 10
6. Question
Which consideration is most important when selecting an approach to SAS Macro for Future Management Management Management Analytics? An internal audit department is evaluating the sustainability of a SAS-based reporting system used for multi-year strategic planning. The system relies on macro-driven data steps to aggregate information from various business units. To ensure that the analytics remain functional and verifiable as the organization grows, the development team must choose a strategy that emphasizes long-term reliability and auditability.
Correct
Correct: In the context of future management analytics and internal auditing, modularity and documentation are the most critical factors. Modular code allows for easier testing, validation, and updates when business rules or data structures change. Documentation ensures that the audit trail is clear and that future programmers can maintain the system without introducing errors, which directly supports the reliability of long-term analytics.
Incorrect: The PUNCH statement is an obsolete feature used for physical punch cards and is not relevant to modern or future analytics. Restricting the use of character functions like SCAN and SUBSTR is unnecessary and counterproductive, as these functions are essential for parsing dynamic data in a macro environment. Prioritizing compatibility with SAS 6.12 by using RANUNI is a regressive strategy that ignores modern statistical improvements and does not address the primary goal of future-proofing analytics for management.
Takeaway: Prioritizing modularity and documentation in SAS macro development ensures that complex analytics remain auditable, maintainable, and adaptable to future organizational changes.
Incorrect
Correct: In the context of future management analytics and internal auditing, modularity and documentation are the most critical factors. Modular code allows for easier testing, validation, and updates when business rules or data structures change. Documentation ensures that the audit trail is clear and that future programmers can maintain the system without introducing errors, which directly supports the reliability of long-term analytics.
Incorrect: The PUNCH statement is an obsolete feature used for physical punch cards and is not relevant to modern or future analytics. Restricting the use of character functions like SCAN and SUBSTR is unnecessary and counterproductive, as these functions are essential for parsing dynamic data in a macro environment. Prioritizing compatibility with SAS 6.12 by using RANUNI is a regressive strategy that ignores modern statistical improvements and does not address the primary goal of future-proofing analytics for management.
Takeaway: Prioritizing modularity and documentation in SAS macro development ensures that complex analytics remain auditable, maintainable, and adaptable to future organizational changes.
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Question 7 of 10
7. Question
Which characterization of SAS Macro for Incident Management Management Management Management Analytics is most accurate for SAS Certified Specialist: Base Programming? An internal auditor is reviewing automated incident logs to assess the effectiveness of IT controls and needs to extract the Risk_Level from a character variable named Audit_Trail, where the data is formatted as Date|User|Risk_Level|Action.
Correct
Correct: The SCAN function is the most efficient method in Base SAS for extracting a specific segment from a delimited string. By specifying the pipe character as the delimiter and the number 3, the function correctly identifies and returns the third component of the Audit_Trail variable, regardless of the length of the preceding segments. This is a fundamental skill for auditors who must parse complex log files to evaluate control data.
Incorrect
Correct: The SCAN function is the most efficient method in Base SAS for extracting a specific segment from a delimited string. By specifying the pipe character as the delimiter and the number 3, the function correctly identifies and returns the third component of the Audit_Trail variable, regardless of the length of the preceding segments. This is a fundamental skill for auditors who must parse complex log files to evaluate control data.
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Question 8 of 10
8. Question
Following an alert related to SAS Macro for Objectives and Key Results Management Management Management Management Management Management Analytics, what is the proper response? An internal auditor is reviewing the automated data transformation process for corporate performance metrics. The audit reveals that several OKR descriptions are being truncated during the execution of a SAS macro that uses the SCAN and SUBSTR functions to parse data from a source file. To ensure data integrity and accurate reporting, which technical control should be verified in the SAS code?
Correct
Correct: In SAS, character variables have a fixed length. When using functions like SCAN or SUBSTR to parse OKR descriptions, if the target variable’s length is not explicitly defined, SAS may assign a default length (often based on the first occurrence or a default of 200 bytes) that is insufficient for the actual data, leading to truncation. Explicitly defining the LENGTH statement in the DATA step ensures that the variable can accommodate the full string length, maintaining data integrity for the analytics process.
Incorrect: Option B is incorrect because using the COMPRESS function to remove delimiters would prevent the SCAN function from identifying the individual tokens within the string. Option C is incorrect because the ROUND function is a numeric function and cannot be applied to character-based descriptions. Option D is incorrect because the UPCASE function is a character function and cannot be applied to numeric variables without causing an implicit type conversion or a syntax error, and it would not address data truncation issues.
Takeaway: Explicitly defining variable lengths in the DATA step is a critical control for preventing data truncation when using character manipulation functions like SCAN and SUBSTR.
Incorrect
Correct: In SAS, character variables have a fixed length. When using functions like SCAN or SUBSTR to parse OKR descriptions, if the target variable’s length is not explicitly defined, SAS may assign a default length (often based on the first occurrence or a default of 200 bytes) that is insufficient for the actual data, leading to truncation. Explicitly defining the LENGTH statement in the DATA step ensures that the variable can accommodate the full string length, maintaining data integrity for the analytics process.
Incorrect: Option B is incorrect because using the COMPRESS function to remove delimiters would prevent the SCAN function from identifying the individual tokens within the string. Option C is incorrect because the ROUND function is a numeric function and cannot be applied to character-based descriptions. Option D is incorrect because the UPCASE function is a character function and cannot be applied to numeric variables without causing an implicit type conversion or a syntax error, and it would not address data truncation issues.
Takeaway: Explicitly defining variable lengths in the DATA step is a critical control for preventing data truncation when using character manipulation functions like SCAN and SUBSTR.
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Question 9 of 10
9. Question
How should SAS Macro for Value Chain Analysis Management Management Management Analytics be correctly understood for SAS Certified Specialist: Base Programming? When reviewing an automated audit routine for value chain efficiency, a programmer needs to capture a specific threshold value from a summary dataset—such as the 90th percentile of Operations costs—and pass this value to a macro variable so it can be used as a filter in subsequent steps. Which approach correctly implements this requirement within the scope of Base SAS programming?
Correct
Correct: The CALL SYMPUTX routine is the standard method in Base SAS to assign a value from a DATA step variable to a macro variable during the execution phase. It is preferred over CALL SYMPUT because it automatically trims leading and trailing blanks from the value, which is essential for clean macro variable substitution in value chain reporting and filtering.
Incorrect: The %LET statement is a macro trigger that is processed at compile time; it cannot be used to assign the value of a dataset variable to a macro variable during the execution of a DATA step. The SYMPUT function (or CALL SYMPUTX) must be used within a DATA step or a specialized procedure, but PROC MEANS does not support a SYMPUT statement directly in its syntax. The INTO clause is a valid way to create macro variables, but it is specific to PROC SQL and is not a valid statement within a DATA step.
Takeaway: Use CALL SYMPUTX within a DATA step to dynamically transfer data-driven values into the macro facility for automated analysis and reporting.
Incorrect
Correct: The CALL SYMPUTX routine is the standard method in Base SAS to assign a value from a DATA step variable to a macro variable during the execution phase. It is preferred over CALL SYMPUT because it automatically trims leading and trailing blanks from the value, which is essential for clean macro variable substitution in value chain reporting and filtering.
Incorrect: The %LET statement is a macro trigger that is processed at compile time; it cannot be used to assign the value of a dataset variable to a macro variable during the execution of a DATA step. The SYMPUT function (or CALL SYMPUTX) must be used within a DATA step or a specialized procedure, but PROC MEANS does not support a SYMPUT statement directly in its syntax. The INTO clause is a valid way to create macro variables, but it is specific to PROC SQL and is not a valid statement within a DATA step.
Takeaway: Use CALL SYMPUTX within a DATA step to dynamically transfer data-driven values into the macro facility for automated analysis and reporting.
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Question 10 of 10
10. Question
Serving as compliance officer at a mid-sized retail bank, you are called to advise on SAS Macro for PESTLE Analysis Management Management Management Management Analytics during periodic review. The briefing a transaction monitoring alert has identified that character data from various jurisdictions is being truncated during the aggregation process. To resolve this, you must determine the actual length of the ‘Political_Risk_Factor’ variable for each record, excluding any trailing spaces, to ensure the macro-driven reporting engine allocates sufficient buffer. Which SAS character function is designed to return this value?
Correct
Correct: The LENGTH function returns the length of a character string, excluding trailing blanks. In the context of a compliance review for PESTLE analysis, accurately determining the length of risk factor descriptions is crucial for ensuring that data is not lost or truncated when processed by automated macros or stored in fixed-length variables.
Incorrect: SUBSTR is used to extract a portion of a string based on a starting position and length, not to find the total length. SCAN is used to select a specific word or segment from a string using delimiters. VERIFY checks a string against a set of characters and returns the position of the first character that is not in the set, which is used for content validation rather than length measurement.
Incorrect
Correct: The LENGTH function returns the length of a character string, excluding trailing blanks. In the context of a compliance review for PESTLE analysis, accurately determining the length of risk factor descriptions is crucial for ensuring that data is not lost or truncated when processed by automated macros or stored in fixed-length variables.
Incorrect: SUBSTR is used to extract a portion of a string based on a starting position and length, not to find the total length. SCAN is used to select a specific word or segment from a string using delimiters. VERIFY checks a string against a set of characters and returns the position of the first character that is not in the set, which is used for content validation rather than length measurement.
