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Valid DA0-002 Exam Sims, DA0-002 Valid Real Exam
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Free PDF 2025 CompTIA Efficient DA0-002: Valid CompTIA Data+ Exam (2025) Exam Sims
The CompTIA DA0-002 certification provides is beneficial to accelerate your career in the tech sector. Today, the CompTIA certification is a fantastic choice to get high-paying jobs and promotions, and to achieve it, you must crack the challenging DA0-002 Exam. It is critical to prepare with actual CompTIA Data+ Exam (2025) (DA0-002) exam questions if you have less time and want to clear the test in a short time.
CompTIA Data+ Exam (2025) Sample Questions (Q81-Q86):
NEW QUESTION # 81
Which of the following is the best tool for creating a dynamic dashboard?
- A. SAS
- B. Excel
- C. RStudio
- D. Power BI
Answer: D
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 # 82
Given the following table:
ID
Value
1
1.5
2
24.456
3
113
Which of the following data types should an analyst use for the numeric values in the Value column?
- A. Double
- B. Integer
- C. Float
- D. Boolean
Answer: C
Explanation:
This question falls under theData Concepts and Environmentsdomain of CompTIA Data+ DA0-002, focusing on selecting appropriate data types for a given dataset. The Value column contains decimal numbers (1.5, 24.456, 113), requiring a data type that supports such values.
* Double (Option A): Double is a floating-point data type that supports decimals with higher precision than Float, but it's often overkill for typical datasets unless very high precision is needed, which isn't indicated here.
* Float (Option B): Float is a floating-point data type that supports decimal numbers (e.g., 1.5, 24.456) and is commonly used for such values in databases, making it the best choice.
* Boolean (Option C): Boolean is for true/false values, not numeric data.
* Integer (Option D): Integer is for whole numbers, but the values (e.g., 1.5, 24.456) have decimals, so Integer is not suitable.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like Float for decimal numeric values.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 83
A data analyst receives a new data source that contains employee IDs, job titles, dates of birth, addresses, years of service, and employees' birth months. Which of the following inconsistencies should the analyst identify?
- A. Duplication
- B. Equivalence
- C. Redundancy
- D. Parallel
Answer: C
Explanation:
This question falls under theData Governancedomain, focusing on identifying data quality issues. The dataset includes dates of birth and birth months, which suggests a potential inconsistency.
* Redundancy (Option A): The dataset includes both dates of birth (e.g., 1990-05-15) and birth months (e.g., May), which is redundant because the birth month can be derived from the date of birth, indicating a data quality issue.
* Equivalence (Option B): Equivalence isn't a standard data quality term in this context; it might refer to data matching, which isn't the issue here.
* Parallel (Option C): Parallel isn't a recognized data quality term; it might relate to processing, not data inconsistencies.
* Duplication (Option D): Duplication refers to identical records, but the issue here is redundant fields, not duplicate rows.
The DA0-002 Data Governance domain includes "data quality control concepts," and redundancy is a key inconsistency when the same information is stored in multiple forms unnecessarily.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 84
Which of the following best describes the semi-structured data that is gathered when web scraping?
- A. CSV
- B. JSON
- C. CSS
- D. HTML
Answer: B
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 # 85
A grocery store wants to view the revenue from the previous year, highlighting individual departments.
Which of the following is the most appropriate chart to communicate this information?
- A. Line
- B. Area
- C. Pie
- D. Gantt
Answer: C
Explanation:
This question is part of theVisualization and Reportingdomain, focusing on selecting the appropriate visualization for a given dataset. The grocery store wants to view revenue by department, which requires a chart that shows proportions or comparisons across categories.
* Gantt (Option A): Gantt charts are used for project scheduling, not for comparing revenue across categories.
* Pie (Option B): Pie charts are ideal for showing proportions or percentages of a whole, such as revenue distribution across departments, making this the best choice.
* Area (Option C): Area charts are better for showing trends over time, not static categorical comparisons.
* Line (Option D): Line charts are used for trends over time, not for comparing discrete categories like departments.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization" , and a pie chart is the most appropriate for showing departmental revenue proportions.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting
NEW QUESTION # 86
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