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SASInstitute A00-215 certification exam is an excellent way for individuals to demonstrate their proficiency in SAS programming. SAS Certified Associate: Programming Fundamentals Using SAS 9.4 certification is recognized globally and is highly valued by organizations in various industries. Additionally, the certification provides individuals with an opportunity to enhance their skills and knowledge in SAS programming, making them more marketable and competitive in the job market.
SASInstitute A00-215 certification exam is designed to test a candidate's proficiency in programming fundamentals using SAS 9.4. SAS Certified Associate: Programming Fundamentals Using SAS 9.4 certification is an entry-level certification for individuals who have a basic understanding of programming concepts and are interested in a career in data analysis, data science, or business intelligence. SAS Certified Associate: Programming Fundamentals Using SAS 9.4 certification is highly sought after by organizations looking to hire individuals who have a comprehensive understanding of SAS programming.
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NEW QUESTION # 334
You have a dataset called 'sales' with sales data for different products. You want to calculate the mean, standard deviation, and median sales values for each product category, displaying the mean and standard deviation to three decimal places. The dataset has variables 'product_category', 'product_name', and 'sales_amount'. Which PROC MEANS statement correctly performs this task?
Answer: B
Explanation:
The correct answer is option A. The MAXDEC=3 option within the PROC MEANS statement specifies that all statistics, including the mean and standard deviation, should be displayed with three decimal places. The CLASS statement specifies 'product_category' as the grouping variable, and the VAR statement selects 'sales_amount' for analysis. The OUTPUT statement creates a new dataset called 'summary_report' containing the calculated statistics for each product category Option B tries to use a FORMAT statement to control the formatting of the output variables after the PROC MEANS step, which is not the most efficient approach. Option C uses the MAXDEC option with the variable name, but it is not necessary to specify the variable name since it's applying to all variables within the PROC MEANS step. Option D doesn't use the MAXDEC option, so the output will display the default number of decimal places. Option E uses 'product_name' in the CLASS statement, which is incorrect as the grouping should be by 'product_category' Only option A effectively uses the MAXDEC= option within PROC MEANS to achieve the desired output formatting for the mean and standard deviation while correctly grouping by 'product_category' and calculating the median.
NEW QUESTION # 335
You have a dataset with a variable named PHONE NUMBER' containing phone numbers in various formats (e.g., (123) 456-7890, 123-456-7890, 1234567890). You need to extract the area code from each phone number. Which SAS code snippet accomplishes this using the 'SCAN' function?
Answer: A
Explanation:
The correct code snippet is option E. It uses the 'SCAN' function with the following arguments: 1. The 'PHONE_NUMBER' variable. 2. The first word to extract (1).3. The delimiter 4. The position of the delimiter from the end of the string (1). This extracts the area code, which is enclosed in parentheses, from the phone number. Option A will extract the first word before the hyphen. Option B will extract the first word before a space. Option C will extract the first word before the closing parenthesis_ Option D will extract the last word after an opening parenthesis. Option E correctly extracts the first word before the closing parenthesis from the end of the string.
NEW QUESTION # 336
You have a dataset 'SALES' with variables 'PRODUCT ID', 'SALES DATE', and 'QUANTITY SOLD'. You want to create two new datasets: 'SALES BY PRODUCT and 'SALES BY MONTH'. 'SALES BY PRODUCT' should contain the total quantity sold for each product, and 'SALES BY MONTH' should contain the total quantity sold for each month. Which of the following DATA step code snippets will achieve this correctly?
Answer: D
Explanation:
Option A is the only correct code snippet that correctly creates two datasets, 'SALES_BY_PRODUCT' and 'SALES_BY_MONTH'. It uses the statement with 'PRODUCT_ID' and to group the data, and the function to calculate the total quantity sold for each group. The 'output' statement is used to write the calculated totals to the corresponding datasets. Option B uses the 'output' statement with dataset names, which is not supported in this scenario. Option C uses the '+4 operator instead of the function for calculating totals, which will not work correctly. Option D only calculates the total quantity for the 'SALES BY_PRODUCT dataset and not for Option E is similar to option A but creates only the 'SALES_BY_MONTH' dataset.
NEW QUESTION # 337
You're analyzing a dataset containing customer satisfaction ratings (1-5) and want to create a report showing the frequency distribution of ratings. However, you want the report to display the 'Excellent' (5) rating at the top and 'Poor' (1 ) rating at the bottom. Which PROC FREQ statement would achieve this?
Answer: B
Explanation:
The correct answer is E. The ORDER=(INTERNAL DESCENDING) option sorts the rows in descending order based on the internal storage order of the variable 'Rating'. Since the internal storage order would likely assign lower values to 'Poor' (1) and higher values to 'Excellent' (5), this achieves the desired result of 'Excellent' at the top and 'Poor' at the bottom. The other options are incorrect: A (ORDER-FREQ): Sorts rows by frequency, from highest to lowest. It might not match the desired order. B (ORDER-DATA): Sorts rows based on their order in the dataset. It might not match the desired order. C (ORDER-DESCENDING): Sorts rows by internal storage order but in descending order. This would result in the 'Poor' rating at the top. D (ORDER-FORMATTED): Sorts rows based on the formatted values, which might not match the desired order.
NEW QUESTION # 338
Which statement is true regarding a DATA step concatenation?
Answer: B
Explanation:
In a DATA step concatenation in SAS, when multiple datasets are listed in a SET statement, SAS concatenates the datasets vertically, stacking them one on top of the other. The length of variables in the resulting dataset is determined by the first dataset that appears in the SET statement. If the same variable appears in multiple datasets, SAS uses the length as it is first encountered. It does not require columns with the same name to be renamed; rather, it stacks them directly. There is no maximum number of tables that can be listed in a SET statement for concatenation; more than two can be concatenated. Lastly, concatenation combines data vertically, not horizontally as option D suggests.
References
* SAS 9.4 Language Reference: Concepts, "Concatenating Data Sets."
NEW QUESTION # 339
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