DA0-001 Exam Dumps - CompTIA Data+ Certification Exam

The measure of dispersion is used to describe the spread of data around a central value. In the context of a data set containing actual and projected sales, the measure of dispersion will help to understand the variability or consistency of sales figures. The variance is themost appropriate statistical method for finding the measure of dispersion because it calculates the average of the squared differences from the Mean, providing a clear picture of data spread. It is especially useful in comparing the spread between different data sets and understanding the distribution of data points.

Mean is a measure of central tendency, not dispersion.

Correlation measures the relationship between two variables, not the spread of a single variable.

Confidence intervals are used to estimate the range within which a population parameter will fall, but they do not measure dispersion within the data set itself.

Explanation

The best answer is to use a watermark to identify the report as a draft. A watermark is a faint image or text that appears behind the content of a document, indicating its status or ownership. By using a watermark, Kelly can clearly communicate that the report is not final and still subject to changes or feedback. This can prevent confusion among the readers and avoid any misuse or misinterpretation of the report. The other options are not as effective as using a watermark, as they either do not indicate the status of the report or do not reach the appropriate stakeholders. Distributing the report via email or publishing it onan internally facing website may not make it clear that the report is a draft and may cause confusion or errors. Showing the report to her immediate supervisor may not get enough feedback from other relevant stakeholders who may have different perspectives or insights. Reference: How to Add a Watermark inMicrosoft Word - Lifewire

To get the total quantity sold by each employee, you need:

An aggregate function to compute the total per group.

A clause to define the grouping (i.e., “by each employee”).

The SQL pattern is generally:

SELECT employee_id,

SUM(quantity) AS total_quantity

FROM sales

GROUP BY employee_id;

SUM (F) is the aggregate function that adds up all quantity values.

GROUP BY (D) groups the rows by employee so that SUM is calculated per employee.

Why the other options are incorrect:

MAX (A) returns the maximum value, not the total quantity.

SELECT (B) is a SQL keyword for selecting columns, not an aggregate function.

HAVING (C) is used to filter groups after aggregation, not to perform aggregation itself.

ORDER BY (E) sorts the result set; it does not calculate totals.

Thus, the functions needed to retrieve the total quantity sold by each employee are GROUP BY (D) and SUM (F).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Data manipulation: aggregate functions and GROUP BY.

CompTIA Data+ SQL sections: examples combining SUM with GROUP BY to summarize data by category (e.g., employee, product, region).

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.