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CompTIA Data+ Exam (2025) Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which of the following is the best tool for creating a dynamic dashboard?

A. Excel
B. RStudio
C. Power BI
D. SAS

Answer: C

Explanation:
The question asks for the best tool to create adynamic dashboard, which falls under theVisualization and Reportingdomain of CompTIA Data+ DA0-002. According to the DA0-002 draft objectives, this domain includes understanding tools and techniques for creating effective visualizations, such as dashboards, that can be updated dynamically to reflect real-time or changing data. A dynamic dashboard typically allows for interactivity, real-time updates, and user-driven exploration of data, which is a key focus in this domain.
* Power BI (Option A): Power BI is a business intelligence tool by Microsoft designed specifically for creating interactive and dynamic dashboards. It supports real-time data updates, user interactivity (e.g., filters, slicers), and integration with various data sources, making it ideal for dynamic dashboard creation.
* RStudio (Option B): RStudio is primarily an IDE for the R programming language, used for statistical computing and data analysis. While it can create visualizations, it's not optimized for dynamic dashboards without additional packages like Shiny, and even then, it requires more coding effort compared to Power BI.
* Excel (Option C): Excel is a spreadsheet tool that can create static charts and basic dashboards, but it lacks the interactivity and real-time update capabilities of a true dynamic dashboard tool like Power BI.
* SAS (Option D): SAS is a statistical analysis software suite that excels in data mining and analytics but is not primarily designed for creating dynamic, interactive dashboards.
The DA0-002 Visualization and Reporting domain emphasizes tools that facilitate "the appropriate visualization in the form of a report or dashboard with the proper design components," as noted in similar DA0-001 objectives (web ID: 1). Power BI aligns best with this requirement due to its focus on dynamic, user- friendly dashboard creation.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting

NEW QUESTION # 27
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: A

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 # 28
Which of the following best describes the semi-structured data that is gathered when web scraping?

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

Answer: A

Explanation:
This question pertains to theData Acquisition and Preparationdomain, which in DA0-002 includes understanding data acquisition concepts and the types of data gathered from varioussources, such as web scraping. Web scraping involves extracting data from websites, and the data gathered is often semi-structured, meaning it has some organizational structure but isn't fully relational like a database table.
* JSON (Option A): JSON (JavaScript Object Notation) is a semi-structured data format commonly used in web applications. Web scraping often retrieves data in JSON format via APIs or embedded scripts, as it's lightweight and structured with key-value pairs, making it ideal for semi-structured data.
* CSV (Option B): CSV (Comma-Separated Values) is a structured format typically used for tabular data. It's not commonly the direct output of web scraping, though scraped data might be converted to CSV later.
* CSS (Option C): CSS (Cascading Style Sheets) is used for styling web pages and isn't a data format, making it irrelevant for describing scraped data.
* HTML (Option D): HTML (HyperText Markup Language) is the structure of web pages and is often the raw format scraped during web scraping. While HTML is semi-structured due to its tag-based hierarchy, it's primarily a markup language, not a data format, and the actual data extracted is often parsed into formats like JSON.
The DA0-002 Data Acquisition and Preparation domain aligns with the DA0-001 focus on "data acquisition concepts" (web ID: 14), which includes identifying formats like JSON as semi-structured data commonly acquired through web scraping. JSON is the best fit here due to its prevalence in web data exchange.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation

NEW QUESTION # 29
Which of the following explains the purpose of UAT?

A. To validate and verify that a software application meets the needs and requirements of users
B. To review software application crashes, create patches, and deploy to users
C. To ensure all parts of the software application work together after each sprint
D. To begin the software application development process to enhance user experience

Answer: A

Explanation:
This question is related to theData Governancedomain of DA0-002, which includes understanding processes like User Acceptance Testing (UAT) to ensure data-related applications meet governance and quality standards. UAT is a critical step in ensuring software aligns with user needs and organizational requirements.
* To begin the software application development process to enhance user experience (Option A):
UAT occurs near the end of development, not at the beginning.
* To ensure all parts of the software application work together after each sprint (Option B): This describes integration testing, not UAT, which focuses on user validation.
* To review software application crashes, create patches, and deploy to users (Option C): This refers to post-deployment maintenance, not UAT.
* To validate and verify that a software application meets the needs and requirements of users (Option D): UAT is specifically designed to ensure the software meets user requirements and functions as intended in a real-world scenario, aligning with governance standards for quality.
The DA0-002 Data Governance domain emphasizes "data quality control concepts" (similar to DA0-001, web ID: 1), which include ensuring applications meet user needs through processes like UAT.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance

NEW QUESTION # 30
A data analyst receives a flat file that includes dates. The analyst needs to calculate the number of days from the dates on the file to the current date. Which of the following is the best way to complete this task?

A. Transform data to a numerical value and use mathematical functions.
B. Use date functions to analyze the data with no conversion.
C. Validate the date format with logical functions and use date functions to analyze.
D. Convert data to date format and use date functions.

Answer: D

Explanation:
This question pertains to theData Analysisdomain, focusing on date calculations. The task is to calculate the difference between dates in a file and the current date, requiring proper date handling.
* Convert data to date format and use date functions (Option A): Flat files often store dates as strings (e.g., "2023-01-01"). Converting them to a date format (e.g., using Python's datetime or SQL's TO_DATE) allows the use of date functions (e.g., DATEDIFF) to calculate the difference to the current date, which is the best approach.
* Validate the date format with logical functions and use date functions to analyze (Option B):
Validation is unnecessary if conversion handles format issues, making this overly complex.
* Use date functions to analyze the data with no conversion (Option C): Without converting to a date format, date functions may fail if the data is stored as strings.
* Transform data to a numerical value and use mathematical functions (Option D): This is inefficient and error-prone compared to using date functions.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and converting to date format followed by date functions is the standard method for such calculations.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

NEW QUESTION # 31
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