Free Practice Questions for CompTIA DA0-001 Exam

To compare the students’ scores, we need to standardize them by using the z-score formula, which is:

z = (x - μ) / σ

where x is the raw score, μ is the mean, and σ is the standard deviation. The z-score tells us how many standard deviations a score is above or below the mean. A higher z-score means a better score relative to the average.

Using the table, we can calculate the z-scores for each student as follows:

Randy: z = (76 - 70) / 2 = 3 Katie: z = (86 - 80) / 3 = 2 Ralph: z = (80 - 75) / 2 = 2.5 Jean: z = (80 - 90) / 1 = -10

The student with the highest z-score is Randy, with a z-score of 3. This means that Randy scored 3 standard deviations above the mean in math, which is the best performance among the four students. Therefore, the correct answer is A.

VARCHARis a data type optimized for storingvariable-length strings. It only uses the necessary space for each value, making it more efficient in terms of storage compared to CHAR, which allocates a fixed amount of space regardless of the string's actual length​.

When updating a legacy data source, it is a best practice to create a codebook to document field changes. A codebook serves as a detailed guide and record of the data structure, definitions, and any transformations or modifications made to the data fields. This documentation is crucial for maintaining data integrity, ensuring consistency, and facilitating future data use and understanding. It provides a reference that can be invaluable for data analysts, developers, and any stakeholders who need to work with the data.

Creating a codebook is preferred over placing old data in new fields, which can lead to confusion and data integrity issues. Keeping only the most recent data may result in the loss of valuable historical information. Removing the data source from production is not a practice related to updating data but rather to retiring a data source1234.

When an analyst observes changes in sales ratios over a period, such as in a quarterly report, they are conducting a trend analysis. Trend analysis is a statistical method used to examine and evaluate the movement of data points over time to identify patterns or trends. This type of analysis is particularly useful for forecasting future events based on historical data. It differs from gap analysis, which assesses the difference between actual performance and potential or desired performance; link analysis, which is used to find associations among data; and statistical analysis, which is a broad term for all types of data analysis methods, including trend analysis.

A logical function is a type of function that returns a value based on a condition or a set of conditions. For example, the IF function in Excel can be used to check if a certain condition is met, and then return one value if true, and another value if false. In this case, the data analyst can use a logical function to check if the Quantity_sold column is greater than 1,000,000, and then return “Yes” if true, and “No” if false. This would create a new variable called Promotion_flag that indicates whether the salesperson has sold more than 1,000,000 units or not. References: CompTIA Data+ Certification Exam Objectives, Logical functions (reference)

When designing a database field to capture a binary preference, such as whether a customer likes vanilla ice cream, the most appropriate data type is:

Option B:Boolean

Rationale:A Boolean data type is used to represent binary values, typically TRUE or FALSE. In this context, it efficiently captures whether a customer likes (TRUE) or does not like (FALSE) vanilla ice cream.

Option A:Integer

Rationale:While integers represent whole numbers, using them to denote binary choices (e.g., 1 for "likes" and 0 for "dislikes") is less intuitive and can lead to ambiguity without proper context.

Option C:Categorical

Rationale:Categorical data types are used for fields that can take on one of a limited set of values, representing different categories. While "likes" and "dislikes" could be categories, a Boolean is more efficient for binary choices.

Option D:Numeric

Rationale:Numeric data types encompass both integers and floating-point numbers. Using a numeric type for a binary preference is unnecessary and could lead to data integrity issues.

This is because date is a type of variable that represents a specific point or period in time, such as a day, a month, or a year. Date variables can be used to store, manipulate, or analyze temporal data, such as transaction dates, birth dates, or expiration dates. For example, date variables can be used to calculate the duration or the difference between two dates, or to filter or sort the data by date. The other variable types are not correct descriptions of the “Year” column. Here is why:

Numeric is a type of variable that represents a numerical value, such as an integer, a decimal, or a fraction. Numeric variables can be used to store, manipulate, or analyze quantitative data, such as amounts, prices, or scores. For example, numeric variables can be used to perform arithmetic operations or calculations on the data, or to measure the central tendency or the dispersion of the data.

Alphanumeric is a type of variable that represents a combination of alphabetic and numeric characters, such as letters, numbers, symbols, or spaces. Alphanumeric variables can be used to store, manipulate, or analyze textual data, such as names, addresses, or codes. For example, alphanumeric variables can be used to concatenate or split the data, or to search or match the data using patterns or expressions.

Text is a type of variable that represents a sequence of alphabetic characters, such as letters or words. Text variables can be used to store, manipulate, or analyze textual data, such as names, categories, or labels. For example, text variables can be used to change the case or the length of the data, or to compare or classify the data using criteria or rules.