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CompTIA Data+ Exam (2025) Sample Questions (Q84-Q89):

NEW QUESTION # 84
A data analyst receives the following sales data for a convenience store:
Item
Quantity
Price
Chocolate Bars
7
$1.99
Vanilla Ice Bars
2
$4.99
Chocolate Wafers
6
$0.99
Peanut Butter
2
$2.99
Cups
3
$4.99
Strawberry Jam
3
$4.99
Chocolate Cake
9
$6.99
Milk Chocolate
2
$2.99
Almonds
5
$2.99
The analyst needs to provide information on the products that contain chocolate. Which of the following RegEx should the analyst use to filter the chocolate products?

A. #Chocolate#$
B. Chocolate$
C. %Chocolate&
D. Chocolate!

Answer: B

Explanation:
This question falls under theData Acquisition and Preparationdomain, which includes techniques for manipulating and filtering data, such as using regular expressions (RegEx) to identify specific patterns in text data. The task is to filter items containing the word "Chocolate."
* Chocolate! (Option A): In RegEx, "!" is not a valid pattern for matching a word like "Chocolate." It typically denotes negation in some contexts, but here it's incorrect.
* Chocolate$ (Option B): The "$" in RegEx anchors the pattern to the end of the string, meaning it matches "Chocolate" at the end of an item name (e.g., "Milk Chocolate"). This is the most appropriate pattern for identifying items ending with "Chocolate," which applies to the relevant items in the list.
* %Chocolate& (Option C): "%" and "&" are not standard RegEx anchors; they're often used in SQL LIKE patterns, not RegEx, making this incorrect.
* #Chocolate#$ (Option D): "#" is not a standard RegEx anchor, and this pattern would look for
"Chocolate" surrounded by "#", which doesn't match the data.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation" , and RegEx is a common technique for filtering text data. The pattern "Chocolate$" correctly identifies items like
"Chocolate Bars," "Chocolate Wafers," "Chocolate Cake," and "Milk Chocolate." Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation

NEW QUESTION # 85
A data analyst needs to join together a table data source and a web API data source using Python. Which of the following is the best way to accomplish this task?

A. Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together.
B. Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together.
C. Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together.
D. Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together.

Answer: D

Explanation:
This question falls under theData Acquisition and Preparationdomain of CompTIA Data+ DA0-002, which involves acquiring and combining data from different sources, such as a database and a web API, using tools like Python. The task requires joining the data, which in Python often involves using pandas DataFrames.
* Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together (Option A): VARCHAR is a databasedata type for strings, not a format for data exchange or merging in Python, making this incorrect.
* Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together (Option B): Web APIs commonly return data in JSON format, and databases can export data as JSON. In Python, JSON data can be easily converted to pandas DataFrames using pandas.read_json() or pandas.DataFrame(), and then merged using pandas.merge() on a common key, making this the best approach.
* Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together (Option C): TXT is a generic text format that lacks structure, making it less efficient for merging compared to JSON.
* Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together (Option D): Converting to a string format is vague and not a standard approach for structured data merging in Python.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as combining data from APIs and databases, and JSON is a standard format for this purpose in Python.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.

NEW QUESTION # 86
Which of the following best describes the method used to combine files, software, and libraries for use on various operating systems and environments?

A. Virtual machine
B. Containerization
C. Package manager
D. Code repository

Answer: B

Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on methods for managing software and data environments. The task is to identify a method that combines files, software, and libraries for use across different systems.
* Package manager (Option A): Package managers (e.g., npm) manage software dependencies but don't combine files and libraries for cross-system use.
* Code repository (Option B): Code repositories (e.g., GitHub) store code but don't package it for deployment across environments.
* Virtual machine (Option C): Virtual machines emulate entire operating systems, which is heavier than needed for combining files and libraries.
* Containerization (Option D): Containerization (e.g., Docker) packages files, software, and libraries into a container that can run consistently across different operating systems and environments, making it the best choice.
The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and containerization is a standard method for ensuring consistency across systems.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 87
The director of operations at a power company needs data to help identify where company resources should be allocated in order to monitor activity for outages and restoration of power in the entire state. Specifically, the director wants to see the following:
* County outages
* Status
* Overall trend of outages
INSTRUCTIONS:
Please, select each visualization to fit the appropriate space on the dashboard and choose an appropriate color scheme. Once you have selected all visualizations, please, select the appropriate titles and labels, if applicable.
Titles and labels may be used more than once.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

Answer:

Explanation:
Power outages
Explanation:
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.

NEW QUESTION # 88
Which of the following file types separates data using a delimiter?

A. CSV
B. HTML
C. JSON
D. XML

Answer: A

Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on understanding file formats and their structures. The task is to identify a file type that uses delimiters to separate data.
* XML (Option A): XML uses tags to structure data, not delimiters.
* HTML (Option B): HTML is a markup language for web pages, not a data file format using delimiters.
* JSON (Option C): JSON uses key-value pairs and nested structures, not delimiters like commas.
* CSV (Option D): CSV (Comma-Separated Values) uses delimiters (typically commas) to separate data fields, making it the correct choice.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as file formats like CSV that use delimiters.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 89
......

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