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CompTIA DA0-002 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA Data+ Certification Exam
Exam Number:DA0-002
Exam Duration:90 minutes
Exam Price:$253 USD (may vary by region/tax)
Exam Format:Performance-based questions, Multiple-response, Multiple-choice
Certificate Validity Period:3 years
Passing Score:675 (on a scale of 100–900)
Real Exam Qty:Up to 90
Available Languages:English
Recommended Training:CompTIA Data+ Study Resources
CompTIA CertMaster Learn for Data+
Exam Registration:Pearson VUE CompTIA Exams Registration
CompTIA Data+ Certification Page
Sample Questions:CompTIA DA0-002 Sample Questions
Exam Way:Delivered via Pearson VUE testing centers or online proctored exam
Pre Condition:No mandatory prerequisites. Recommended: 18–24 months of experience in a business/data analytics environment.
Official Syllabus URL:https://www.comptia.org/certifications/data-plus

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CompTIA - Updated DA0-002 - CompTIA Data+ Exam Test Result

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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • This section of the exam measures skills of a Data Governance Associate and introduces principles for keeping data secure, accurate, and compliant. It covers access controls, encryption, classification of sensitive data like PII and PHI, and legal requirements tied to data use. Candidates must know how to apply quality checks, validate data, and manage master data effectively. It also addresses best practices for maintaining integrity through data dictionaries, audits, and standardisation processes.
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
  • 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.

CompTIA Data+ Exam Sample Questions (Q197-Q202):

NEW QUESTION # 197
Given the following tables:
Individual table
ID
FirstName
LastName
1
John
Doe
Output
ID
FullName
1
JohnDoe
Which of the following is the best option to display output from FirstName and LastName as FullName?

Answer: C

Explanation:
This question falls under theData Acquisition and Preparationdomain of CompTIA Data+ DA0-002, focusing on data manipulation techniques. The task is to combine FirstName and LastName into a single FullName field (e.g., "JohnDoe").
* Concatenate (Option A): Concatenation combines two or more strings into one (e.g., usingCONCAT in SQL or "+" in Python), which is the correct method to create FullName from FirstName and LastName.
* Filter (Option B): Filtering selects specific rows based on conditions, not suitable for combining fields.
* Join (Option C): Joining combines data from multiple tables, but the task involves manipulating data within a single table.
* Group (Option D): Grouping (e.g., GROUP BY in SQL) is for aggregation, not for combining fields into a new column.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and concatenation is the standard technique for combining fields like FirstName and LastName into FullName.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.


NEW QUESTION # 198
Which of the following is a data warehouse term for a data set specifically created for drill-down reports?

Answer: A

Explanation:
A cube organizes data in multiple dimensions and supports drill-down analysis, allowing users to explore data at different levels of detail.


NEW QUESTION # 199
A data analyst wants to find the mean score that students received in science classes. Given the following tables:

Which of the following will produce the desired results?

Answer: A


NEW QUESTION # 200
A data analyst is creating a dashboard for a group of visually impaired stakeholders. Which of the following communication approaches is best?

Answer: C

Explanation:
Incorporating audio accessibility functions ensures that visually impaired users can interpret and interact with dashboard content independently, aligning with accessibility best practices.


NEW QUESTION # 201
Given the following table:

Which of the following data types should an analyst use for the numeric values in the Value column?

Answer: D

Explanation:
A double is the most appropriate data type because the Value column contains very large numbers in scientific notation as well as decimal values that require high precision. A double- precision floating-point type can accurately store both large and fractional numeric values.


NEW QUESTION # 202
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