A data analyst receives a new data source that contains employee IDs, job titles, dates of birth, addresses, years of service, and employees?? birth months. Which of the following inconsistencies should the analyst identify?
Correct Answer:A
This question falls under theData Governancedomain, focusing on identifying data quality issues. The dataset includes dates of birth and birth months, which suggests a potential inconsistency.
✑ Redundancy (Option A): The dataset includes both dates of birth (e.g., 1990-05-
15) and birth months (e.g., May), which is redundant because the birth month can be derived from the date of birth, indicating a data quality issue.
✑ Equivalence (Option B): Equivalence isn??t a standard data quality term in this
context; it might refer to data matching, which isn??t the issue here.
✑ Parallel (Option C): Parallel isn??t a recognized data quality term; it might relate to processing, not data inconsistencies.
✑ Duplication (Option D): Duplication refers to identical records, but the issue here is redundant fields, not duplicate rows.
The DA0-002 Data Governance domain includes "data quality control concepts," and redundancy is a key inconsistency when the same information is stored in multiple forms unnecessarily.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
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Before distributing a report, a marketing analyst notices that the total distinct promotional email messages is less than the combined total of emails sent. Which of the following is the most likely reason for this difference?
Correct Answer:D
This question falls under theData Analysisdomain, focusing on analyzing discrepancies in data reports. The total distinct messages are fewer than the total emails sent, indicating a specific issue.
✑ The aggregation did not include all emails (Option A): If the aggregation missed
emails, the total sent would be lower, not the distinct count.
✑ Some emails were not delivered (Option B): Undelivered emails would reduce the total sent, but the scenario implies the total sent is accurate.
✑ The report failed to run properly (Option C): A report failure would likely cause broader issues, not a specific discrepancy between distinct and total counts.
✑ A recipient received duplicate emails (Option D): If recipients received duplicates, the total emails sent would be higher than the distinct messages (unique email content), explaining the difference.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and identifying duplicates is a common analysis task to explain such discrepancies.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
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The human resources department wants to understand the relationship between the ages and incomes of all employees. Which of the following graphics is the most appropriate to present the analysis?
Correct Answer:A
This question pertains to theVisualization and Reportingdomain, focusing on selecting the appropriate visualization to show a relationship between two continuous variables (ages and incomes).
✑ Scatter plot (Option A): A scatter plot displays individual data points on two axes
(age vs. income), making it ideal for showing the relationship and potential correlation between two continuous variables.
✑ Area plot (Option B): Area plots are used for showing trends over time, not
relationships between two variables.
✑ Bar chart (Option C): Bar charts are better for categorical data comparisons, not continuous variable relationships.
✑ Pie chart (Option D): Pie charts show proportions of a whole, not suitable for showing relationships between variables.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a scatter plot is best for showing the relationship between age and income.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
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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?
Correct Answer:D
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.
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A data analyst receives a flat file that includes dates. The analyst needs to calculate the number of days from the dates on the file to the current date. Which of the following is the best way to complete this task?
Correct Answer:A
This question pertains to theData Analysisdomain, focusing on date calculations. The task is to calculate the difference between dates in a file and the current date, requiring proper date handling.
✑ Convert data to date format and use date functions (Option A): Flat files often store
dates as strings (e.g., "2023-01-01"). Converting them to a date format (e.g., using Python??s datetime or SQL??s TO_DATE) allows the use of date functions (e.g., DATEDIFF) to calculate the difference to the current date, which is the best approach.
✑ Validate the date format with logical functions and use date functions to analyze
(Option B): Validation is unnecessary if conversion handles format issues, making this overly complex.
✑ Use date functions to analyze the data with no conversion (Option C): Without
converting to a date format, date functions may fail if the data is stored as strings.
✑ Transform data to a numerical value and use mathematical functions (Option D): This is inefficient and error-prone compared to using date functions.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and converting to date format followed by date functions is the standard method for such calculations.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
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