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CompTIA Data+ Exam (2025) latest study torrent & DA0-002 vce dumps & DA0-002 practice cram

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

NEW QUESTION # 86
A data analyst must combine service calls into low-, medium-, and high-priority levels in order to analyze organizational responses. Which of the following techniques should the analyst use for this task?

A. Imputation
B. Binning
C. Augmentation
D. Scaling

Answer: B

Explanation:
This question pertains to theData Analysisdomain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.
* Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.
* Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.
* Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.
* Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a standard technique for categorizing data for analysis.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

NEW QUESTION # 87
Which of the following best describes the reason an analyst would reference a data dictionary versus a source's metadata?

A. To gather information about the availability of the data
B. To find the content and specific attributes for a dataset
C. To find a summary of basic information about the dataset
D. To gather information and resources about the data

Answer: B

Explanation:
This question is part of theData Concepts and Environmentsdomain, focusing on the purpose of data documentation tools like data dictionaries and metadata. The question compares their uses.
* To gather information and resources about the data (Option A): This is too vague and not specific to a data dictionary's purpose.
* To find the content and specific attributes for a dataset (Option B): A data dictionary provides detailed definitions of data elements (e.g., field names, types, descriptions), which is more specific than metadata, which often includes broader information like creation date or source.
* To find a summary of basic information about the dataset (Option C): This better describes metadata, which provides high-level summaries, not detailed attributes.
* To gather information about the availability of the data (Option D): Neither a data dictionary nor metadata typically focuses on data availability.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is specifically used to find detailed attributes of a dataset.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 88
A data analyst needs to modify a dashboard that was created by another employee. Upon opening the dashboard, the analyst notices that the information is not loading properly. Which of the following should the analyst do to troubleshoot this error?

A. Validate that the database is up-to-date.
B. Ensure the correct filters are displaying on the dashboard.
C. Review the data layer and data source.
D. Check that the program is updated to the latest version.

Answer: C

Explanation:
This question falls under theData Governancedomain of CompTIA Data+ DA0-002, focusing on troubleshooting data quality issues in dashboards. The dashboard isn't loading properly, indicating a potential issue with the data connection or configuration.
* Review the data layer and data source (Option A): The data layer (e.g., queries, connections) and data source (e.g., database) are the foundation of a dashboard. If the information isn't loading, the issue likely lies in the data connection or query configuration, making this the first step.
* Validate that the database is up-to-date (Option B): While this might be a subsequent step, it assumes the connection is working, which should be confirmed first.
* Check that the program is updated to the latest version (Option C): Software updates might fix bugs, but this isn't the most immediate cause of data not loading.
* Ensure the correct filters are displaying on the dashboard (Option D): Filters affect data display, but if the data isn't loading at all, the issue is more fundamental.
The DA0-002 Data Governance domain includes "data quality control concepts," and reviewing the data layer and source is the primary step in troubleshooting dashboard loading issues.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

NEW QUESTION # 89
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. Standardization
B. Concatenate
C. Binning
D. Appending

Answer: C

Explanation:
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.

NEW QUESTION # 90
A data analyst is working on an initial analysis of the dataset in the following table:
DateTime
Count
2024-01-01
12
2024-01-02
245
2024-01-02
13
2024-01-03
13
2024-01-03
12
00:00:00
12
Which of the following issues should the analyst flag in the data report?

A. Completeness
B. Outlier
C. Duplication
D. Mismatch

Answer: B

Explanation:
This question falls under theData Analysisdomain, focusing on identifying data quality issues.The table shows counts over time, and the analyst needs to flag an issue in the data.
* Completeness (Option A): Completeness refers to missing data, but all rows have values for DateTime and Count.
* Outlier (Option B): The count of 245 on 2024-01-02 is significantly higher than other counts (12-13), indicating an outlier that should be investigated for accuracy.
* Mismatch (Option C): Mismatch implies inconsistent data types or formats, but the DateTime and Count columns appear consistent except for the last row (addressed separately).
* Duplication (Option D): Duplication refers to identical rows, but no rows are identical (same DateTime and Count).
The last row ("00:00:00", 12) has a formatting issue, but the most significant issue for analysis is the outlier (245), as it could skew results. The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as identifying outliers in datasets.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

NEW QUESTION # 91
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