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CompTIA DA0-002日本語資格取得 & DA0-002日本語関連対策
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DA0-002日本語関連対策、DA0-002復習攻略問題
当社CompTIAのDA0-002学習教材は、試験に合格するための最高のDA0-002試験トレントを提供するのに十分な自信を持っています。長年の実務経験により、市場の変化とニーズに迅速に対応しています。このようにして、最新のDA0-002ガイドトレントがあります。市場動向に遅れずについていく方法について心配する必要はありません。 DA0-002試験問題は、受験者がDA0-002試験に合格するのに最も適していると言えます。後悔することはありません。
CompTIA Data+ Exam (2025) 認定 DA0-002 試験問題 (Q26-Q31):
質問 # 26
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 DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS
- C. SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
- D. SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
正解：C
解説：
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.
質問 # 27
A data analyst calculated the average score per student without making any changes to the following table:
Student
Subject
Score
123
Math
100
123
Biology
80
234
Math
96
123
Biology
80
345
Biology
88
234
Math
96
Which of the following exploration techniques should the analyst have considered before calculating the average?
- A. Grouping
- B. Duplication
- C. Binning
- D. Redundancy
正解：B
解説：
This question pertains to theData Governancedomain, focusing on data quality issues that affect analysis.
The table contains duplicate rows, which would skew the average score calculation if not addressed.
* Student 123: Math (100), Biology (80), Biology (80)- Duplicate Biology score.
* Student 234: Math (96), Math (96)- Duplicate Math score.
* Student 345: Biology (88)- No duplicates.
* Duplication (Option A): The table has duplicate rows (e.g., Student 123's Biology score of 80 appears twice), which would inflate the average if not removed. The analyst should have checked for duplicates before calculating the average.
* Redundancy (Option B): Redundancy refers to unnecessary fields (e.g., storing the same data in multiple columns), not duplicate rows.
* Binning (Option C): Binning groups data into categories, not relevant for addressing duplicates in averaging.
* Grouping (Option D): Grouping (e.g., GROUP BY in SQL) might be part of the solution,but the issue to identify is duplication.
The DA0-002 Data Governance domain includes "data quality control concepts," and checking for duplication is critical to ensure accurate calculations like averages.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
質問 # 28
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. Binning
- B. Appending
- C. Standardization
- D. Concatenate
正解：A
解説：
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.
質問 # 29
A company reports on seven years of data in a sales dashboard. The dashboard pulls from a sales database that has 30 years of data. The dashboard performance is slow. Which of the following is the best way to improve the dashboard's performance?
- A. Filtering to include only relevant data
- B. Performing a code review
- C. Checking network connectivity
- D. Adding more RAM and rerunning
正解：A
解説：
This question falls under theData Governancedomain, focusing on optimizing data quality and performance in dashboards. The dashboard is slow because it pulls from a large database (30 years) but only needs seven years of data.
* Performing a code review (Option A): A code review might identify inefficiencies, but it's not the most direct solution for this scenario.
* Checking network connectivity (Option B): Network issues might cause delays, but the primary issue is the data volume, not connectivity.
* Filtering to include only relevant data (Option C): Filtering the data to include only the last seven years reduces the dataset size, directly improving performance by minimizing the data processed.
* Adding more RAM and rerunning (Option D): Adding RAM might help, but it's a hardware solution that doesn't address the root cause of excessive data.
The DA0-002 Data Governance domain includes "data quality control concepts," such as optimizing performance by filtering data to improve efficiency.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
質問 # 30
A data analyst learns that a report detailing employee sales is reflecting sales only for the current month.
Which of the following is the most likely cause?
- A. An error in SQL code
- B. Connectivity issues
- C. Report refresh failure
- D. Lack of permissions
正解：A
解説：
This question falls under theData Analysisdomain, focusing on troubleshooting issues in data reports. The report should show all employee sales but is limited to the current month, suggesting a data retrieval issue.
* Lack of permissions (Option A): Permissions issues would likely prevent access entirely, not limit data to the current month.
* An error in SQL code (Option B): The report likely uses an SQL query to retrieve data, and an error (e.g., a WHERE clause filtering for the current month) could restrict the data to the current month, making this the most likely cause.
* Report refresh failure (Option C): A refresh failure would result in outdated data, not specifically current-month data.
* Connectivity issues (Option D): Connectivity issues would likely prevent the report fromrunning, not limit it to a specific time frame.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and errors in SQL code are a common cause of incorrect data retrieval in reports.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
質問 # 31
......
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