CompTIA Updated DA0-001 Exam Questions and Answers by casey

When examining tolerances or variation in sensor readings, the key concept is how much measurements deviate from the average. The standard CompTIA approach:

Standard deviation measures the spread of data around the mean.

A small standard deviation ⇒ readings are tightly clustered (low variation).

A large standard deviation ⇒ readings vary widely (high variation), indicating potential issues with sensor consistency.

Why the other options are less appropriate:

Median (C) and Mean (D) are measures of central tendency, not dispersion; they tell you about the center, not how tightly values cluster around it.

Quartile range (B) (or interquartile range) is also a dispersion measure, but standard deviation is the classic choice when dealing with continuous measurements and tolerances in engineering/QA contexts.

Thus, to evaluate the tolerances in IoT sensor readings, Standard deviation (A) is the best measure.

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Data analysis: measures of central tendency and dispersion (standard deviation, variance, range, IQR).

CompTIA Data+ Study content: standard deviation is emphasized as a primary measure of spread for continuous data.

Parsing is the data manipulation technique that should be used to separate the column into multiple columns for each component of the name. Parsing is the process of breaking down a string of text into smaller units, such as words, symbols, or numbers. Parsing can be used to extract specific information from a text column, such as names, addresses, phone numbers, etc. Parsing can also be used to split a text column into multiple columns based on a delimiter, such as a comma, space, or dash1. In this case, the analyst can use parsing to split the column by the comma delimiter and create three new columns: one for the last name, one for the first name, and one for the middle initial. This will make the data more organized and easier to analyze.

The Chi-squared test is used to analyze relationships between two categorical variables. In this case, age groups and voting preferences are both categorical variables, making chi-squared the most appropriate test​.