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| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - 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.
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| 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.
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| Topic 4 | - 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.
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| Topic 5 | - 5.0 Data Governance, Quality, and Controls
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CompTIA Data+ Exam Sample Questions (Q119-Q124):
NEW QUESTION # 119
A data analyst needs to create a combined report that includes information from the following two tables:

Which of the following query methods should the analyst use for this task?
- A. Group
- B. Nested
- C. Join
- D. Union
Answer: D
Explanation:
A union combines rows from two tables with the same structure into a single result set, making it appropriate for merging managers and non-managers into one report.
NEW QUESTION # 120
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?
- A. Appending
- B. Standardization
- C. Concatenate
- D. Binning
Answer: D
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 # 121
A data analyst needs to identify outliers from a given data set. Which of the following visualizations is the best way to identify outliers?
- A. Box plot
- B. Scatter plot
- C. Gantt chart
- D. Waterfall chart
Answer: A
Explanation:
A box plot is specifically designed to display the distribution of data and highlight outliers. Values that fall significantly above or below the typical range are shown separately from the whiskers, making outliers easy to identify.
NEW QUESTION # 122
Which of the following statistical measures is the most suitable and simple measure of the strengthsimple measure of the strength and direction of the linear relationship between price and volume?
- A. Covariance
- B. Correlation
- C. Interception
- D. Standard deviation
Answer: B
Explanation:
Correlation measures both the strength and direction of a linear relationship between two variables, such as price and volume.
NEW QUESTION # 123
A data analyst is creating a forecast for a product line introduced early last year. Which of the following should the analyst use to create projected sales and customer satisfaction for next year?
- A. Numerical and ordinal attributes
- B. Boolean data and an array
- C. Standard deviation and constraints
- D. Mean and median
Answer: A
Explanation:
This question pertains to theData Analysisdomain, focusing on data types and methods for forecasting. The task involves projecting sales (numerical) and customer satisfaction (likely ordinal,e.g., ratings), requiring appropriate data attributes.
* Standard deviation and constraints (Option A): Standard deviation measures data spread, and constraints are conditions, neither of which directly supports forecasting.
* Mean and median (Option B): Mean and median are descriptive statistics, not sufficient for forecasting future values.
* Boolean data and an array (Option C): Boolean data (true/false) and arrays (data structures) are not relevant for forecasting sales and satisfaction.
* Numerical and ordinal attributes (Option D): Sales are numerical (e.g., units sold), and customer satisfaction is often ordinal (e.g., 1-5 ratings). These attributes are suitable for forecasting models (e.g., time-series analysis for sales, regression for satisfaction).
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and numerical and ordinal attributes are key for forecasting sales and satisfaction.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 124
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