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CompTIA DA0-002 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA Data+ Exam
Exam Number:DA0-002
Exam Price:$255 USD
Exam Duration:90 minutes
Exam Format:Performance-based questions, Multiple-choice
Certificate Validity Period:3 years
Available Languages:English
Passing Score:720 (scale 100–900)
Real Exam Qty:Up to 90
Recommended Training:CompTIA Official Training
Exam Registration:CompTIA Official Registration
Pearson VUE Testing
Sample Questions:CompTIA DA0-002 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:Recommended 18–24 months of experience in data/business analysis role; familiarity with databases, analytical tools, basic statistics, and visualization
Official Syllabus URL:https://www.comptia.org/certifications/data

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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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.

CompTIA Data+ Exam Sample Questions (Q159-Q164):

NEW QUESTION # 159
A database administrator needs to implement security triggers for an organization's user information database. Which of the following data classifications is the administrator most likely using? (Select two).

Answer: E,F


NEW QUESTION # 160
A data analyst receives a request for the current employee head count and runs the following SQL statement:
SELECT COUNT(EMPLOYEE_ID) FROM JOBS
The returned head count is higher than expected because employees can have multiple jobs. Which of the following should return an accurate employee head count?

Answer: D

Explanation:
This question falls under theData Analysisdomain of CompTIA Data+ DA0-002, which involves using SQL queries to analyze data and address issues like duplicates in datasets. The issue here is that the initial query counts all instances of EMPLOYEE_ID in the JOBS table, but employees can have multiple jobs, leading to an inflated head count. The goal is to count unique employees.
* SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS (Option A): This query is syntactically incorrect because COUNT DISTINCT(EMPLOYEE_ID) should use parentheses as COUNT(DISTINCT EMPLOYEE_ID). It also groups by JOB_TYPE, which is unnecessary for a total head count.
* SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS (Option B): This query is incorrect because DISTINCT applies to the rows returned, not the COUNT function directly. It doesn't address the duplicate EMPLOYEE_ID issue.
* SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option C): While this query correctly uses COUNT(DISTINCT EMPLOYEE_ID) to count unique employees, grouping by JOB_TYPE breaks the count into separate groups, which isn't required for a total head count.
* SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option D): This query correctly counts only unique EMPLOYEE_IDs by using the DISTINCT keyword within the COUNT function, providing an accurate total head count without grouping.
The DA0-002 Data Analysis domain emphasizes "given a scenario, applying the appropriate descriptive statistical methods using SQL queries," which includes handling duplicates with functions like COUNT (DISTINCT). Option D is the most direct and accurate method for a total unique head count.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 161
A data analyst is creating a high-level report for the Chief Executive Officer. Which of the following data would most likely be included in the report?

Answer: D

Explanation:
Year-to-date sales numbers provide high-level performance insights appropriate for executive decision-making.


NEW QUESTION # 162
Which of the following explains the difference between a char and an nchar data field?

Answer: C

Explanation:
Both char and nchar are fixed-length data types. The difference is that nchar stores Unicode characters, allowing support for a wider range of languages and symbols. Because Unicode characters typically require two bytes per character, nchar uses twice the storage space of char.


NEW QUESTION # 163
A data analyst must combine service calls into low-, medium-, and high-priority levels in order to analyze organizational responses. Which of the following techniques should the analyst use for this task?

Answer: B

Explanation:
This question pertains to theData Analysisdomain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.
* Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.
* Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.
* Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.
* Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a standard technique for categorizing data for analysis.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 164
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