認定するDA0-002日本語版試験勉強法一回合格-素晴らしいDA0-002復習過去問
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CompTIA DA0-002 認定試験の出題範囲:
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CompTIA Data+ Exam (2025) 認定 DA0-002 試験問題 (Q117-Q122):
質問 # 117
A data professional wants to identify all customers who made a purchase in January. Given the following table:
CustomerID
Month
Sales
0001
January
13000
0002
March
10000
0003
April
23000
0004
May
10000
Which of the following types of functions should the professional use to flag the customers?
- A. Date
- B. Mathematical
- C. Statistical
- D. Logical
正解:D
解説:
This question falls under theData Analysisdomain, focusing on selecting the appropriate function type to filter data in a query. The task is to flag customers who made a purchase in January, which involves a conditional check.
* Statistical (Option A): Statistical functions (e.g., AVG, STDEV) analyze data distributions, not suitable for flagging specific months.
* Logical (Option B): Logical functions (e.g., WHERE Month = 'January' in SQL) are used to apply conditions and flag rows based on criteria, which fits the task.
* Mathematical (Option C): Mathematical functions (e.g., SUM, ROUND) perform calculations, not conditional flagging.
* Date (Option D): Date functions (e.g., MONTH()) manipulate dates, but the Month column is already in text format, so a logical comparison is sufficient.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and logical functions are best for conditional flagging.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
質問 # 118
A data analyst is analyzing the following dataset:
Transaction Date
Quantity
Item
Item Price
12/12/12
11
USB Cords
9.99
11/11/11
3
Charging Block
8.89
10/10/10
5
Headphones
50.15
Which of the following methods should the analyst use to determine the total cost for each transaction?
- A. Deriving
- B. Compressing
- C. Scaling
- D. Parsing
正解:A
解説:
This question falls under theData Analysisdomain, focusing on calculating new values from existing data.
The task is to determine the total cost per transaction, which involves multiplying Quantity by Item Price.
* Parsing (Option A): Parsing involves breaking down data (e.g., splitting a string), not calculating totals.
* Scaling (Option B): Scaling adjusts numerical values to a common range (e.g., normalization), not relevant for calculating totals.
* Compressing (Option C): Compressing reduces data size, not applicable to calculating costs.
* Deriving (Option D): Deriving involves creating new data fields by performing calculations on existing ones (e.g., Total Cost = Quantity × Item Price), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as deriving new fields through calculations to analyze data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
質問 # 119
The director of operations at a power company needs data to help identify where company resources should be allocated in order to monitor activity for outages and restoration of power in the entire state. Specifically, the director wants to see the following:
* County outages
* Status
* Overall trend of outages
INSTRUCTIONS:
Please, select each visualization to fit the appropriate space on the dashboard and choose an appropriate color scheme. Once you have selected all visualizations, please, select the appropriate titles and labels, if applicable.
Titles and labels may be used more than once.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
正解:
解説:
Power outages
Explanation:
This is a simulation question that requires you to create a dashboard with visualizations that meet the director' s needs. Here are the steps to complete the task:
* Drag and drop the visualization that shows the county outages on the top left space of the dashboard.
This visualization is a map of the state with different colors indicating the number of outages in each county. You can choose any color scheme that suits your preference, but make sure that the colors are consistent and clear. For example, you can use a gradient of red to show the counties with more outages and green to show the counties with less outages.
* Drag and drop the visualization that shows the status of the outages on the top right space of the dashboard. This visualization is a pie chart that shows the percentage of outages that are active, restored, or pending. You can choose any color scheme that suits your preference, but make sure that the colors are distinct and easy to identify. For example, you can use red for active, green for restored, and yellow for pending.
* Drag and drop the visualization that shows the overall trend of outages on the bottom space of the dashboard. This visualization is a line graph that shows the number of outages over time. You can choose any color scheme that suits your preference, but make sure that the color is visible and contrasted with the background. For example, you can use blue for the line and white for the background.
* Select appropriate titles and labels for each visualization. Titles and labels may be used more than once.
For example, you can use "County Outages" as the title for the map, "Status" as the title for the pie chart, and "Trend" as the title for the line graph. You can also use "County", "Number of Outages",
"Active", "Restored", "Pending", "Time", and "Number of Outages" as labels for the axes and legends of the visualizations.
質問 # 120
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?
- A. Some emails were not delivered.
- B. The aggregation did not include all emails.
- C. A recipient received duplicate emails.
- D. The report failed to run properly.
正解:C
解説:
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.
質問 # 121
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 CROSS JOIN SELECT * FROM Managers
- B. SELECT * FROM Employees JOIN SELECT * FROM Managers
- C. SELECT * FROM Employees UNION ALL SELECT * FROM Managers
- D. SELECT * FROM Employees UNION SELECT * FROM Managers
正解:D
解説:
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.
質問 # 122
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