DA0-001 Exam Dumps - CompTIA Data+ Questions and Answers

If a single data set was split into two files, both files typically:

Have the same columns/structure

Contain different rows (e.g., File 1 has rows 1–10,000; File 2 has rows 10,001–20,000)

The correct way to restore the original data set is to stack the rows from one file underneath the rows from the other. This operation is called:

Appending (or “union” in SQL terms):

You keep the same schema (same columns)

You increase the number of rows

Other options:

Merging usually joins based on a key column (like customer_id) to bring in additional columns.

Blending often refers to combining data from different sources on related keys, especially in BI tools.

Aggregating reduces detail (e.g., summing or grouping), which is the opposite of what we want here.

Since we simply want to rebuild the full data set from two vertical slices, the correct technique is Append (B).

CompTIA Data+ Reference (concept alignment):

CompTIA Data+ Official Exam Objectives – Domain on Data Mining / Data Preparation (combining datasets through appending vs merging).

CompTIA Data+ Official Study Guide – discussion of data append/union vs merge/join when preparing data.

Discrete data is a type of data that can only take certain values, usually whole numbers or integers. Discrete data can be counted, but not measured. For example, the number of students in a class, the number of books in a library, or the number of phone calls that a call center receives in a day are all examples of discrete data. Discrete data is different from continuous data, which can take any value within a range, and can be measured with precision. For example, the height of a person, the weight of a fruit, or the temperature of a room are all examples of continuous data. Therefore, the correct answer is D. References: [Discrete vs Continuous Data: Definition and Examples - Statistics How To], [Discrete Data - Definition and Examples | Math Goodies]

The most likely cause of the issue is that the databases are recording the event in different time zones. A time zone is a region that observes a uniform standard time for legal, commercial, and social purposes. Different time zones have different offsets from Coordinated Universal Time (UTC), which is the primary time standard by which the world regulates clocks and time. For example, UTC-5 is five hours behind UTC, while UTC+3 is three hours ahead of UTC. If an event is being stored in two databases that are housed in different geographical locations with different time zones, it may appear that the event is being logged at different times, depending on how the databases handle the time zone conversion. For example, if one database records the event in UTC-5 and another database records the event in UTC+3, then an event that occurs at 12:00 PM in UTC-5 will appear as 9:00 AM in UTC+3. The other options are not likely causes of the issue, as they are either unrelated or implausible. The data analyst is not querying the databases incorrectly, as this would not affect the time stamps of the events. The databases are not recording different events, as they are supposed to record the same recurring event. The second database is not logging incorrectly, as there is no evidence or reason to assume that.Reference: [Time zone - Wikipedia]

The correct answer is D. Continuous data.

Continuous data is a type of quantitative data that can take any value within a range and can be measured with infinite precision. Continuous data can be expressed as fractions, decimals, or percentages.Examples of continuous data are height, weight, temperature, time, speed, etc12

The duration of a phone call in milliseconds is an example of continuous data, because it can take any value within a range (from zero to infinity) and can be measured with infinite precision (up to milliseconds or even smaller units). The duration of a phone call in milliseconds can also be expressed as fractions, decimals, or percentages of a larger unit (such as seconds, minutes, or hours).

Ordinal data is not correct, because ordinal data is a type of qualitative or categorical data that can be ordered or ranked according to some criterion. Ordinal data can have a logical order, but the intervals between the values are not equal or meaningful.Examples of ordinal data are grades, ratings, ranks, etc12

Nominal data is not correct, because nominal data is a type of qualitative or categorical data that can be labeled or named without any order or ranking. Nominal data can have a finite number of categories or classes, but the categories have no intrinsic value or hierarchy.Examples of nominal data are gender, color, nationality, etc12

Boolean data is not correct, because boolean data is a type of binary data that can have only two possible values: true or false. Boolean data can be used to represent logical statements, conditions, or outcomes. Examples of boolean data are yes/no, on/off, 1/0, etc.

A waterfall chart is a type of data visualization that shows the cumulative impact of sequentially introduced values that are positive or negative. A waterfall chart typically has an initial value and a final value, with intermediate values shown as floating columns that either add to or subtract from the initial value. A waterfall chart can help visualize how different factors contribute to a net change in a value over time. Therefore, the correct answer is B. References: [Waterfall Chart | Definition & Examples - Investopedia], [Waterfall Charts in Excel | How to Create Waterfall Chart in Excel?]

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The value that will appear first if they are sorted in descending order is Xavier. Descending order means arranging values from the largest to the smallest, or from the last to the first in alphabetical order. In this case, Xavier is the last name in alphabetical order, so it will appear first when sorted in descending order. The other names will appear in the following order: Molly, Adam, Aaron. Reference: Sorting Data - W3Schools

A discrete data type is a data type that can only take on a finite number of values, such as integers or categories. An example of a discrete data type is the number of kids, as it can only be a whole number. The other options are examples of continuous data types, as they can take on any value within a range. The length in inches or centimeters, the distance in miles or kilometers, and the weight in pounds or kilograms are all continuous data types. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

This is a simulation question that requires you to create a dashboard with visualizations that meet the director’s needs. Here are the steps to complete the task:

Drag and drop the visualization that shows the county outages on the top left space of the dashboard. This visualization is a map of the state with different colors indicating the number of outages in each county. You can choose any color scheme that suits your preference, but make sure that the colors are consistent and clear. For example, you can use a gradient of red to show the counties with more outages and green to show the counties with less outages.

Drag and drop the visualization that shows the status of the outages on the top right space of the dashboard. This visualization is a pie chart that shows the percentage of outages that are active, restored, or pending. You can choose any color scheme that suits your preference, but make sure that the colors are distinct and easy to identify. For example, you can use red for active, green for restored, and yellow for pending.

Drag and drop the visualization that shows the overall trend of outages on the bottom space of the dashboard. This visualization is a line graph that shows the number of outages over time. You can choose any color scheme that suits your preference, but make sure that the color is visible and contrasted with the background. For example, you can use blue for the line and white for the background.

Select appropriate titles and labels for each visualization. Titles and labels may be used more than once. For example, you can use “County Outages” as the title for the map, “Status” as the title for the pie chart, and “Trend” as the title for the line graph. You can also use “County”, “Number of Outages”, “Active”, “Restored”, “Pending”, “Time”, and “Number of Outages” as labels for the axes and legends of the visualizations.

The SQL query provided is selecting all records from the Cust_table where the age column has values greater than 60 and the City column matches “New York”. The > operator selects values that are strictly greater than the comparison value, so it does not include customers aged exactly 60. The term “New York” in the context of a city database typically refers to New York City, not the state of New York. Therefore, the correct answer is that the analyst is retrieving data for all customers older than age 60 in New York City.

Explanation

Correct answer A. Sample.

Jenny does not have data for the entire population of all undergraduate sophomores. While a specific grade point average is an observation of variable, jenny needs sample data.