DA0-002 VALID EXAM PATTERN, DA0-002 INTERACTIVE PRACTICE EXAM

DA0-002 Valid Exam Pattern, DA0-002 Interactive Practice Exam

DA0-002 Valid Exam Pattern, DA0-002 Interactive Practice Exam

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Tags: DA0-002 Valid Exam Pattern, DA0-002 Interactive Practice Exam, DA0-002 Valid Exam Materials, DA0-002 Technical Training, DA0-002 Premium Exam

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CompTIA Data+ Exam (2025) Sample Questions (Q86-Q91):

NEW QUESTION # 86
Which of the following is the best tool for creating a dynamic dashboard?

  • A. RStudio
  • B. SAS
  • C. Power BI
  • D. Excel

Answer: C

Explanation:
The question asks for the best tool to create adynamic dashboard, which falls under theVisualization and Reportingdomain of CompTIA Data+ DA0-002. According to the DA0-002 draft objectives, this domain includes understanding tools and techniques for creating effective visualizations, such as dashboards, that can be updated dynamically to reflect real-time or changing data. A dynamic dashboard typically allows for interactivity, real-time updates, and user-driven exploration of data, which is a key focus in this domain.
* Power BI (Option A): Power BI is a business intelligence tool by Microsoft designed specifically for creating interactive and dynamic dashboards. It supports real-time data updates, user interactivity (e.g., filters, slicers), and integration with various data sources, making it ideal for dynamic dashboard creation.
* RStudio (Option B): RStudio is primarily an IDE for the R programming language, used for statistical computing and data analysis. While it can create visualizations, it's not optimized for dynamic dashboards without additional packages like Shiny, and even then, it requires more coding effort compared to Power BI.
* Excel (Option C): Excel is a spreadsheet tool that can create static charts and basic dashboards, but it lacks the interactivity and real-time update capabilities of a true dynamic dashboard tool like Power BI.
* SAS (Option D): SAS is a statistical analysis software suite that excels in data mining and analytics but is not primarily designed for creating dynamic, interactive dashboards.
The DA0-002 Visualization and Reporting domain emphasizes tools that facilitate "the appropriate visualization in the form of a report or dashboard with the proper design components," as noted in similar DA0-001 objectives (web ID: 1). Power BI aligns best with this requirement due to its focus on dynamic, user- friendly dashboard creation.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting


NEW QUESTION # 87
A data analyst is creating a new dataset that involves bringing together the following datasets:
Name
ID
Date of birth
Frank
23525
3/19
Martha
11290
6/13
Ellen
12141
11/4
ID
Address
City
State
23525
1234 Harding
Chicago
IL
11040
935 Terrace Hills
Chino
CA
11290
2 Speedway
Miami
FL
Which of the following would be the output if the data analyst does a FULL JOIN?

  • A. Name
    ID
    Date of birth
    Address
    City
    State
    Frank
    23525
    3/19
    1234 Harding
    Chicago
    IL
    Martha
    11290
    6/13
    935 Terrace Hills
    Chino
    CA
    Ellen
    12141
    11/4
    2 Speedway
    Miami
    FL
  • B. Name
    ID
    Date of birth
    Address
    City
    State
    Frank
    23525
    3/19
    1234 Harding
    Chicago
    IL
    Martha
    11290
    6/13
    2 Speedway
    Miami
    FL
    Ellen
    12141
    11/4
    11040
    935 Terrace Hills
    Chino
    CA
  • C. Name
    ID
    Date of birth
    Address
    City
    State
    Frank
    23525
    3/19
    1234 Harding
    Chicago
    IL
    Martha
    11290
    6/13
    2 Speedway
    Miami
    FL
    Ellen
    12141
    11/4
    935 Terrace Hills
    Chino
    CA
  • D. Name
    ID
    Date of birth
    Address
    City
    State
    Frank
    23525
    3/19
    1234 Harding
    Chicago
    IL
    Martha
    11290
    6/13
    935 Terrace Hills
    Chino
    CA
    Ellen
    12141
    11/4
    2 Speedway
    Miami
    FL

Answer: B

Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on database operations like joins. A FULL JOIN combines all rows from both tables, including matches and non-matches, filling in NULLs where there's no corresponding data.
* The first table has IDs: 23525 (Frank), 11290 (Martha), 12141 (Ellen).
* The second table has IDs: 23525, 11040, 11290.
* A FULL JOIN includes all IDs: 23525, 11290, 12141, 11040.
* 23525 matches (Frank with 1234 Harding, Chicago, IL).
* 11290 matches (Martha with 2 Speedway, Miami, FL).
* 12141 has no match in the second table, so Address, City, and State are NULL.
* 11040 has no match in the first table, so Name and Date of birth are NULL.
* Option A: Incorrect; it includes a row for Ellen with "2 Speedway," but Ellen's ID (12141) doesn't match any address, and 11040 is missing.
* Option B: Identical to Option A, so incorrect for the same reasons.
* Option C: Incorrect; it mismatches addresses (e.g., Ellen with 935 Terrace Hills, which belongs to
11040).
* Option D: Correct; it includes all IDs, with NULLs for non-matching rows (Ellen has no address, and
11040 has no name).
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as performing joins in relational databases.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.


NEW QUESTION # 88
A data analyst receives four files that need to be unified into a single spreadsheet for further analysis. All of the files have the same structure, number of columns, and field names, but each file contains different values.
Which of the following methods will help the analyst convert the files into a single spreadsheet?

  • A. Merging
  • B. Parsing
  • C. Appending
  • D. Clustering

Answer: C

Explanation:
This question is part of theData Acquisition and Preparationdomain, which involves combining data from multiple sources. The files have the same structure but different values, meaning theyneed to be stacked vertically into one dataset.
* Merging (Option A): Merging typically involves joining datasets on a common key (e.g., a customer ID), which isn't indicated here since the files only differ in values, not keys.
* Appending (Option B): Appending stacks datasets vertically, combining rows from files with the same structure into a single dataset, which matches the scenario.
* Parsing (Option C): Parsing involves breaking down data (e.g., splitting text), not combining files.
* Clustering (Option D): Clustering is a machine learning technique for grouping similar data points, not for combining files.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as appending datasets with identical structures.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.


NEW QUESTION # 89
A data analyst needs to create a report that anticipates the number of calls received daily. Which of the following is the best statistical method to use?

  • A. Predictive
  • B. Inferential
  • C. Diagnostic
  • D. Descriptive

Answer: A

Explanation:
This question falls under theData Analysisdomain, focusing on statistical methods for forecasting. The task is to anticipate (predict) the number of daily calls, which involves looking into the future.
* Predictive (Option A): Predictive analytics uses historical data to forecast future outcomes (e.g., number of calls), which matches the requirement.
* Diagnostic (Option B): Diagnostic analytics identifies causes and patterns in historical data, not future predictions.
* Inferential (Option C): Inferential statistics make generalizations about a population, not specific forecasts.
* Descriptive (Option D): Descriptive analytics summarizes past data, not suitable for anticipating future values.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and predictive analytics is the best method for forecasting future call volumes.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 90
Given the following table:
ID
Value
1
1.5
2
24.456
3
113
Which of the following data types should an analyst use for the numeric values in the Value column?

  • A. Boolean
  • B. Float
  • C. Integer
  • D. Double

Answer: B

Explanation:
This question falls under theData Concepts and Environmentsdomain of CompTIA Data+ DA0-002, focusing on selecting appropriate data types for a given dataset. The Value column contains decimal numbers (1.5, 24.456, 113), requiring a data type that supports such values.
* Double (Option A): Double is a floating-point data type that supports decimals with higher precision than Float, but it's often overkill for typical datasets unless very high precision is needed, which isn't indicated here.
* Float (Option B): Float is a floating-point data type that supports decimal numbers (e.g., 1.5, 24.456) and is commonly used for such values in databases, making it the best choice.
* Boolean (Option C): Boolean is for true/false values, not numeric data.
* Integer (Option D): Integer is for whole numbers, but the values (e.g., 1.5, 24.456) have decimals, so Integer is not suitable.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like Float for decimal numeric values.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.


NEW QUESTION # 91
......

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