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| Topic | Details |
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| 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.
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| 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.
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| Topic 3 | - 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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CompTIA Data+ Exam Sample Questions (Q59-Q64):
NEW QUESTION # 59
A data analyst needs to remove all duplicate values between two tables, "Employees" and "Managers," using SQL SELECT statements. Which of the following should the analyst use for this task?
- A. SELECT * FROM Employees JOIN SELECT * FROM Managers
- B. SELECT * FROM Employees CROSS JOIN SELECT * FROM Managers
- C. SELECT * FROM Employees UNION SELECT * FROM Managers
- D. SELECT * FROM Employees UNION ALL SELECT * FROM Managers
Answer: C
Explanation:
This question pertains to theData Acquisition and Preparationdomain, focusing on combining and deduplicating data using SQL. The task is to remove duplicates between two tables, meaning the analyst needs a unique set of records from both.
* SELECT * FROM Employees UNION ALL SELECT * FROM Managers (Option A): UNION ALL combines all rows from both tables, including duplicates, which doesn't meet the requirement.
* SELECT * FROM Employees UNION SELECT * FROM Managers (Option B): UNION combines rows from both tables and automatically removes duplicates, providing a unique set of records, which fits the task.
* SELECT * FROM Employees JOIN SELECT * FROM Managers (Option C): This syntax is incorrect; a JOIN requires an ON clause, and it wouldn't remove duplicates.
* SELECT * FROM Employees CROSS JOIN SELECT * FROM Managers (Option D): A CROSS JOIN creates a Cartesian product, resulting in all possible combinations, not removing duplicates.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and UNION is the correct SQL operation for combining tables while removing duplicates.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
NEW QUESTION # 60
An analyst is recording the following financial transaction data with a vendor:
59.99, 49.50, 70.49
Which of the following data types is the best way to record this information?
- A. Numeric
- B. Currency
- C. Continuous
- D. Discrete
Answer: B
Explanation:
Currency data type is specifically designed to store monetary values with appropriate precision and formatting for financial transactions.
NEW QUESTION # 61
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?
- A. SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
- B. SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS
- C. SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
- D. SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS
Answer: C
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 # 62
Which of the following tools would a data analyst most likely use to modify a simple data connection property in a configuration file?
- A. SSMS
- B. Notepad++
- C. RStudio
- D. Toad
Answer: B
Explanation:
Notepad++ is a lightweight text editor commonly used to open and modify configuration files, including simple data connection properties.
NEW QUESTION # 63
A data analyst is generating a custom report for a Chief Executive Officer's executive meeting.
Later, the analyst learns that other custom reports will be required for future executive meetings.
Which of the following delivery methods should the analyst use?
- A. Real-time
- B. Recurring
- C. Ad hoc
- D. Self-service
Answer: B
NEW QUESTION # 64
......
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