DA0-001 Exam Dumps - CompTIA Data+ Certification Exam

 Data cleansing is a critical process in data analytics to ensure the accuracy and quality of data. The reasons to undertake data cleansing include:

Missing Data (B): Missing data can lead to incomplete analysis and biased results. It is essential to identify and address gaps in the dataset to maintain the integrity of the analysis1.

Invalid Data (D): Invalid data includes entries that are out of range, improperly formatted, or illogical (e.g., a negative age). Such data can corrupt analysis and lead to incorrect conclusions1.

Other options, such as non-parametric data (A), are not inherently errors but refer to a type of data that doesn’t assume a normal distribution. Duplicate data © and redundant data (E) could also be reasons for data cleansing, but they are not listed as options to select from in the provided image details. Normalized data (F) refers to data that has been processed to fit into a certain range or format and is typically not a reason for data cleansing.

References:

Understanding the importance of data quality and the impacts of missing and invalid data on research outcomes1.

Best practices in data cleansing2.

Data cleansing is required for various reasons, two of which are missing data (B) and invalid data (D). From the table provided, we can infer the necessity of cleansing in the context of ensuring data integrity and consistency. Missing data refers to the absence of data where it is expected, which can hinder analysis due to incomplete information. Invalid data refers to data that is incorrect, out of range, or in an inappropriate format, which can lead to inaccuracies in any analysis or report. Both these issues can significantly affect the outcomes of any data-related operations and thus need to be rectified through the data cleansing process.

The logical statement that results in Table B is Option D. Option D is a logical statement that uses the AND operator to combine two conditions: Name = “Tom” and Region = “BC”. The AND operator returns true only if both conditions are true, otherwise it returns false. Therefore, Option D will select only the rows from Table A that satisfy both conditions, which are rows 4, 5, 6, and 7. These rows form Table B, as shown below:

Name | Gender flag | Level | College | Code | Region Tom | Male | Elementary | A | BC | BC Kim | Female | Elementary | A | BC | BC Pat | Female | Elementary | A | BC | BC Ben | Male | Elementary | A | BC | BC

The other options are not correct, as they use different logical operators or conditions that do not result in Table B. Option A uses the OR operator, which returns true if either condition is true, or both. Option A will select all the rows from Table A except row 3, which does not match either condition. Option B uses the NOT operator, which returns the opposite of the condition. Option B will select all the rows from Table A except rows 4, 5, 6, and 7, which match the condition. Option C uses a different condition, Region = “ON”, which does not match any row in Table A. Option C will select no rows from Table A. Reference: [SQL Logical Operators - W3Schools]

Gross profit percentage is a key performance indicator (KPI) that measures the profitability of a company’s sales by showing the percentage of revenue that exceeds the cost of goods sold (COGS). It is a critical metric for tracking sales performance because it directly reflects the efficiency of a company in managing its production costs and the profitability of its products. This KPI is essential for understanding the financial health of a business and making informed decisions about pricing, cost control, and sales strategies.

References:

Sales KPIs are essential for measuring the effectiveness of sales activities and the profitability of those efforts1.

Gross profit percentage is highlighted as a crucial metric for assessing the financial success of sales initiatives2.

Understanding the difference between sales metrics and KPIs, and the importance of gross profit percentage as a KPI1.

The significance of gross profit percentage in evaluating sales team performance and guiding business decisions3.

Mean and mode are measures of central tendency, which describe the typical or most common value in a distribution of data. Mean is the arithmetic average of all the values in a dataset, calculated by adding up all the values and dividing by the number of values. Mode is the most frequently occurring value in a dataset. Other measures of central tendency include median, which is the middle value when the data is sorted in ascending or descending order.

The term ‘4Ac1’ is a combination of numbers and letters, which fits the definition of an alphanumeric string. Alphanumeric refers to a character set that contains both letters and numbers. In data analytics and programming, such a value is typically treated as a string, which is a sequence of characters. Strings can include letters, digits, and various other symbols.

A numeric data type would only include numbers, and a float is a specific kind of numeric data type that includes decimal points, neither of which applies to ‘4Ac1’. A boolean data type represents one of two values: true or false. Since ‘4Ac1’ does not represent a true or false value, it cannot be classified as boolean. Lastly, symbolic is not a standard data type in the context of programming and data analytics.

References:

Understanding Python 3 data types1.

Basic Data Types in Python2.

Java Data Types3.