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NEW QUESTION # 143
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?
Answer: B
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 # 144
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?
Answer: D
Explanation:
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.
NEW QUESTION # 145
Which of the following programming languages are the most efficient at performing data mining and statistical analysis? (Choose two.)
Answer: E,F
Explanation:
R is specifically designed for statistical analysis and data mining, offering extensive statistical libraries. Python provides powerful data processing and machine learning libraries, making it highly efficient for data mining and analysis tasks.
NEW QUESTION # 146
A data analyst is cleaning up a data set that contains images. The analysts wants to keep everything that was produced within the last 12 months. Which of the following best describes where the analyst should check for the date of creation on these products?
Answer: B
Explanation:
Metadata contains descriptive information about each file or image, including attributes such as creation date, modification date, file size, and format. To determine which images were produced within the last 12 months, the analyst should examine the metadata of each item rather than the creation date of the dataset or folder containing the files.
NEW QUESTION # 147
Which of the following best describes the reason an analyst would reference a data dictionary versus a source's metadata?
Answer: D
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 # 148
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