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DA0-002熱門題庫，DA0-002參考資料
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最新的 CompTIA Data+ DA0-002 免費考試真題 (Q81-Q86):
問題 #81
A data analyst receives a notification that a customized report is taking too long to load. After reviewing the system, the analyst does not find technical or operational issues. Which of the following should the analyst try next?
- A. Check for data structure changes in the report.
- B. Check that the appropriate filters are applied.
- C. Check data source connections.
- D. Check whether other peers have the same issue.
答案：B
解題說明：
This question pertains to theData Governancedomain, focusing on data quality and report performance optimization. The report is slow despite no technical issues, suggesting a data-related inefficiency.
* Check that the appropriate filters are applied (Option A): Applying filters reduces the dataset size by excluding irrelevant data, improving report performance. This is a logical next step after ruling out technical issues.
* Check data source connections (Option B): The analyst already reviewed the system and found no operational issues, so connectivity is likely not the problem.
* Check for data structure changes in the report (Option C): While possible, this is a deeper investigation step and less likely to be the immediate cause of slowness.
* Check whether other peers have the same issue (Option D): This might confirm the issue's scope but doesn't directly address the performance problem.
The DA0-002 Data Governance domain emphasizes "data quality control concepts," including optimizing report performance through techniques like filtering.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
問題 #82
A data analyst pulls a table similar to the following one:
ID
Type
TypeID
Phone
1
Full Time
Full Time 1
Mobile
2
Part Time
Part Time 2
Work
3
Full Time
Full Time 3
Mobile
Which of the following best explains the data issue with TypeID?
- A. Redundancy
- B. Outlier
- C. Duplication
- D. Missing data
答案：A
解題說明：
This question is part of theData Concepts and Environmentsdomain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.
* Redundancy (Option A): The TypeID column (e.g., "Full Time 1") redundantly includes the Type value ("Full Time") with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.
* Outlier (Option B): Outliers are data points that deviate significantly, which isn't applicable here.
* Missing data (Option C): There are no missing values in the table.
* Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and redundancy is a common data quality issue in schema design.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
問題 #83
A data analyst is gathering data from multiple tables in a database. The analyst needs certain columns from each table. Which of the following is the best method to accomplish this task?
- A. Nest
- B. Aggregate
- C. Union
- D. Join
答案：D
解題說明：
This question falls under theData Acquisition and Preparationdomain, focusing on combining data from multiple tables. The analyst needs specific columns from each table, suggesting a method to combine data horizontally based on relationships.
* Aggregate (Option A): Aggregation (e.g., SUM, COUNT) summarizes data, not suitable for combining columns from tables.
* Union (Option B): Union stacks tables vertically, requiring identical structures, but the analyst needs specific columns, likely based on relationships, not a vertical stack.
* Nest (Option C): Nesting is used for hierarchical data (e.g., JSON), not for combining relational tables.
* Join (Option D): A join (e.g., INNER JOIN) combines tables horizontally based on a common key, allowing the analyst to select specific columns from each table, which fits the task.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and joining tables is the best method for combining specific columns from multiple tables.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
問題 #84
Given the following tables:
Individual table
ID
FirstName
LastName
1
John
Doe
Output
ID
FullName
1
JohnDoe
Which of the following is the best option to display output from FirstName and LastName as FullName?
- A. Group
- B. Filter
- C. Concatenate
- D. Join
答案：C
解題說明：
This question falls under theData Acquisition and Preparationdomain of CompTIA Data+ DA0-002, focusing on data manipulation techniques. The task is to combine FirstName and LastName into a single FullName field (e.g., "JohnDoe").
* Concatenate (Option A): Concatenation combines two or more strings into one (e.g., usingCONCAT in SQL or "+" in Python), which is the correct method to create FullName from FirstName and LastName.
* Filter (Option B): Filtering selects specific rows based on conditions, not suitable for combining fields.
* Join (Option C): Joining combines data from multiple tables, but the task involves manipulating data within a single table.
* Group (Option D): Grouping (e.g., GROUP BY in SQL) is for aggregation, not for combining fields into a new column.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and concatenation is the standard technique for combining fields like FirstName and LastName into FullName.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
問題 #85
An analyst must use the same data to create reports for police patrol supervisors, the city council, and a public website. Which of the following is the best way to differentiate the reports?
- A. Accessibility
- B. Sensitivity
- C. Persona type
- D. Detail levels
答案：C
解題說明：
This question falls under theVisualization and Reportingdomain, focusing on tailoring reports for different audiences. The same data is used for three distinct groups (supervisors, city council, public), requiring differentiation.
* Persona type (Option A): Persona types define the audience's needs and preferences (e.g., supervisors need operational details, the city council needs summaries, the public needs simplified data), making this the best way to differentiate the reports.
* Detail levels (Option B): Detail levels are a result of persona types, not the method of differentiation.
* Accessibility (Option C): Accessibility ensures access for all users (e.g., screen readers), but it's not the primary way to differentiate content.
* Sensitivity (Option D): Sensitivity determines data access (e.g., confidential vs. public), but the scenario implies all reports use the same data, just presented differently.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and persona types guide report differentiation for diverse audiences.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
問題 #86
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DA0-002參考資料: https://www.vcesoft.com/DA0-002-pdf.html
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