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NEW QUESTION # 133
The human resources department wants to know the number of employees who earn $125,000 or more.
However, the department is concerned about duplicates in the dataset. Given the following table:
Employee_ID
Level
Salary
001
1
10000
002
2
20000
003
2
256000
004
2
125000
001
1
10000
002
2
20000
Which of the following SQL statements resolves this issue?
Answer: B
Explanation:
This question falls under theData Analysisdomain, focusing on SQL queries to handle duplicates while counting employees. The task is to count unique employees with a salary of $125,000 or more, addressing duplicates in the dataset.
* Option A: SELECT DISTINCT Employee_ID FROM Employee WHERE Salary >= 125000This lists unique Employee_IDs but doesn't provide a count, which the department needs.
* Option B: SELECT COUNT(DISTINCT Employee_ID) FROM Employee WHERE Salary >=
125000This counts unique Employee_IDs (using DISTINCT) with a salary of $125,000 or more, correctly addressing duplicates and providing the required count (2 employees: 003 and 004).
* Option C: SELECT DISTINCT Employee_ID FROM Employee WHERE Salary > 125000This lists unique Employee_IDs with a salary strictly greater than $125,000 (missing 004), and doesn't provide a count.
* Option D: SELECT COUNT(Employee_ID) FROM Employee WHERE Salary >= 125000This counts all rows without addressing duplicates, resulting in an incorrect count (2 rows, but only 2 unique employees).
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and COUNT(DISTINCT) is the correct method to count unique employees while handling duplicates.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 134
An analyst is developing a form to collect data from clients. Which of the following techniques should the analyst use to best ensure data integrity?
Answer: D
Explanation:
Adding rules for data validation ensures inputs meet expected formats and constraints, preserving data integrity during collection.
NEW QUESTION # 135
Which of the following tables holds relational keys and numeric values?
Answer: A
Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on understanding table types in data warehousing. The task is to identify a table that holds relational keys and numeric values, typically used in a star schema.
* Fact (Option A): Fact tables in a star schema store quantitative data (numeric values, e.g., sales amounts) and foreign keys (relational keys) linking to dimension tables, making this the correct choice.
* Graph (Option B): Graph tables are used in graph databases for relationships (e.g., nodes, edges), not typically for relational keys and numeric values in a traditional sense.
* Dimensional (Option C): Dimension tables store descriptive attributes (e.g., product names) and primary keys, not typically numeric measures.
* Transactional (Option D): Transactional tables are used in OLTP systems and may contain numeric values, but they're not specifically designed for relational keys in a data warehousing context.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and fact tables are designed to hold relational keys and numeric values in a data warehouse.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 136
A data analyst created a dashboard to illustrate the traffic volume and mean response time for a call center.
The traffic data is current, but the mean response time has not updated for more than an hour. Which of the following is the best way to verify the data's freshness?
Answer: D
Explanation:
This question pertains to theData Governancedomain, which in DA0-002 includes ensuring data quality and freshness, especially in dashboards. The issue is that the mean response time isn't updating, while traffic data is current, indicating a potential issue with the data refresh process forthe response time metric.
* Refactoring the code base (Option A): Refactoring might improve long-term performance but doesn't directly address verifying data freshness.
* Testing for network connectivity issues (Option B): Network issues could cause delays, but since traffic data is updating, connectivity is likely not the issue.
* Checking the last time the calculation script ran (Option C): Mean response time is a calculated metric, likely derived from a script. Checking when the script last ran directly verifies if the data refresh process failed, making this the best approach.
* Determining the number of calls with no timestamps (Option D): Missing timestamps might indicate data quality issues, but it doesn't directly verify why the mean response time isn't updating.
The DA0-002 Data Governance domain focuses on "data quality control concepts," including ensuring data freshness in reporting. Checking the script's last run time aligns with this objective.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 137
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?
Answer: D
Explanation:
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.
NEW QUESTION # 138
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