Real CompTIA DA0-002 Questions - Tips And Tricks To Pass Exam

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CompTIA DA0-002 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA Data+ Exam (2025)
Exam Number:DA0-002
Exam Price:USD 255
Real Exam Qty:Up to 90
Certificate Validity Period:3 years
Exam Format:Performance-based, Multiple-choice
Available Languages:English
Related Certifications:CompTIA Data+
Passing Score:720 (scale of 100–900)
Exam Duration:90 minutes
Sample Questions:CompTIA DA0-002 Sample Questions
Exam Way:Online proctored or at Pearson VUE authorized testing centers
Pre Condition:No formal prerequisites; recommended 18–24 months of experience in a report/business analyst role or equivalent knowledge
Official Syllabus URL:https://www.comptia.org/certifications/data

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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Concepts and Environments: This section of the exam measures the skills of a Junior Data Analyst and focuses on understanding core data concepts such as database types, schema structures, and data formats. It highlights differences between structured and unstructured data, compares file types like CSV, JSON, and XML, and introduces key ideas about data dimensions and slowly changing dimensions. Knowing how data is stored and organized helps professionals better prepare for analysis and reporting tasks.
Topic 2
  • Data Analysis: This section of the exam measures skills of a Reporting Analyst and includes foundational knowledge of statistical methods such as averages, variances, and standard deviation. It covers how to use data to find patterns, track performance, and make predictions. This domain also introduces hypothesis testing, regression, correlation, and different types of analysis like exploratory and trend analysis. Candidates should also be aware of common tools used for analysis, including Excel, SQL, Python, R, and popular BI platforms like Tableau and Power BI.
Topic 3
  • Data Mining: This section of the exam measures skills of a Business Intelligence Analyst and covers how data is collected, cleaned, and prepared for analysis. It explains methods like ETL and ELT for data integration, as well as web scraping, API use, and survey data collection. Candidates are expected to identify issues like missing or duplicated data and apply techniques like filtering, sorting, merging, and normalizing. The section also touches on query optimization strategies to improve data handling efficiency.
Topic 4
  • Visualization: This section of the exam measures skills of a Data Visualisation Specialist and focuses on turning raw data into clear, visual insights. It teaches how to match visual formats like bar charts, heat maps, and line graphs to specific audiences and needs. Candidates must understand how to create dashboards and reports using proper design elements such as labels, layout, branding, and colour schemes. This section also includes best practices for dashboard development and delivery through various platforms and user access levels.
Topic 5
  • 5.0 Data Governance, Quality, and Controls

CompTIA Data+ Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
A company gives users adequate data access permissions to allow them to fulfill their duties but nothing more. Which of the following concepts best describes this practice?

Answer: C

Explanation:
The principle of least privilege grants users only the minimum access rights necessary to perform their job responsibilities. This reduces security risks by limiting access to data and systems that are not required for their duties.


NEW QUESTION # 157
Hotspot Question
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
Select each visualization to fit the appropriate space on the dashboard. Once you have selected all visualizations, use the drop-down menus to select the appropriate dashboard titles and axis labels, if applicable.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.


Answer:

Explanation:


NEW QUESTION # 158
Which of the following is the most efficient to use when programming repeatable tasks?

Answer: B

Explanation:
This question pertains to theData Acquisition and Preparationdomain, focusing on tools for automating repeatable tasks in data processes. The task is to identify the most efficient programming method.
* LLM (Option A): Large Language Models (e.g., GPT) are for text generation, not efficient for repeatable data tasks.
* Deep learning (Option B): Deep learning is for complex pattern recognition (e.g., image classification), not efficient for simple repeatable tasks.
* NLP (Option C): Natural Language Processing is for text analysis, not general repeatable tasks.
* RPA (Option D): Robotic Process Automation (RPA) automates repetitive, rule-based tasks (e.g., data entry, file processing), making it the most efficient for programming repeatable tasks.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and RPA is a widely used method for automating repeatable data tasks.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.


NEW QUESTION # 159
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?

Answer: A

Explanation:
An area chart is well suited for displaying revenue over a period of time while also showing the contribution of individual departments to the total. It allows viewers to see overall revenue trends from the previous year and compare how each department contributed to those totals.


NEW QUESTION # 160
A data analyst team needs to segment customers based on customer spending behavior. Given one million rows of data like the information in the following sales order table:
Customer_ID
Region
Amount_spent
Product_category
Quantity_of_items
00123
East
20000
Baby
4
00124
West
30000
Home
6
00125
South
40000
Garden
7
00126
North
50000
Furniture
8
00127
East
60000
Baby
10
Which of the following techniques should the team use for this task?

Answer: A

Explanation:
This question falls under theData Analysisdomain, focusing on techniques for segmenting data. The task is to segment customers based on spending behavior, which involves grouping numerical data (Amount_spent) into categories.
* Standardization (Option A): Standardization scales numerical data to a common range (e.g., z-scores), but it doesn't segment customers into groups.
* Concatenate (Option B): Concatenation combines text fields, not numerical data for segmentation.
* Binning (Option C): Binning involves grouping numerical data into discrete intervals (e.g., low, medium, high spending), which is ideal for segmenting customers based on spending behavior.
* Appending (Option D): Appending combines datasets vertically, not relevant for segmentation.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a common method for segmenting numerical data like spending amounts.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 161
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