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DA0-001: DATA+

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The CompTIA Data+ (DA0-001) exam is a crucial certification for anyone aiming to advance their career in data analysis. Our topic is your ultimate resource for DA0-001 practice test shared by individuals who have successfully passed the exam. These practice tests provide real-world scenarios and invaluable insights to help you ace your preparation.

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  • Comprehensive Coverage: The practice test covers all key topics of the CompTIA DA0-001 exam, including data management, data analysis, and reporting.

  • Customizable Practice: Create your own practice sessions based on specific topics or difficulty levels to tailor your study experience to your needs.

Exam Number: DA0-001

Exam Name: CompTIA Data+

Length of Test: 90 minutes

Exam Format: Multiple-choice, Drag and Drop, and HOTSPOT questions.

Exam Language: English

Number of Questions in the Actual Exam: Maximum of 90 questions

Passing Score: 675/900

Use the shared CompTIA DA0-001 Practice Test to ensure you’re fully prepared for your certification exam. Start practicing today and take a significant step towards achieving your certification goals!

Related questions

Refer to the exhibit.

A data analyst needs to calculate the mean for Q1 sales using the data set below:

CompTIA DA0-001 image Question 1 95175 10022024175129000000

Which of the following is the mean?

$2,466.18
$2,466.18
$2,667.60
$2,667.60
$3,082.72
$3,082.72
$12,330.88
$12,330.88
Suggested answer: C
Explanation:

The mean is the average of all the values in a data set. To calculate the mean, we add up all the values and divide by the number of values. In this case, the mean for Q1 sales is ($2,000 + $3,000 + $4,000 + $2,500 + $3,500) / 5 = $3,082.72 Reference: CompTIA Data+ Certification Exam Objectives, page 9

asked 02/10/2024
Fabrizio Leo
48 questions

A JSON file is an example of:

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Refer to the exhibit.

Given the following data tables:

CompTIA DA0-001 image Question 40 95214 10022024175129000000

Which of the following MDM processes needs to take place FIRST?

Creation of a data dictionary
Creation of a data dictionary
Compliance with regulations
Compliance with regulations
Standardization of data field names
Standardization of data field names
Consolidation of multiple data fields
Consolidation of multiple data fields
Suggested answer: A
Explanation:

This is because a data dictionary is a type of document that defines and describes the data elements, attributes, and relationships in a database or a data set. A data dictionary can be used to facilitate the MDM (Master Data Management) process, which is a process that aims to ensure the quality, consistency, and accuracy of the data across different sources and systems. By creating a data dictionary first, the analyst can establish a common understanding and standardization of the data field names, types, formats, and meanings, as well as identify any potential issues or conflicts in the data, such as missing values, duplicate values, or inconsistent values. The other MDM processes can take place after creating a data dictionary. Here is why:

Compliance with regulations is a type of MDM process that ensures that the data meets the legal and ethical requirements and standards of the industry or the organization. Compliance with regulations can take place after creating a data dictionary, because the data dictionary can help the analyst to identify and apply the relevant rules and policies to the data, such as data privacy, security, or retention.

Standardization of data field names is a type of MDM process that ensures that the data field names are consistent and uniform across different sources and systems. Standardization of data field names can take place after creating a data dictionary, because the data dictionary can provide a reference and a guideline for naming and labeling the data fields, as well as resolving any discrepancies or ambiguities in the data field names.

Consolidation of multiple data fields is a type of MDM process that combines or merges the data fields from different sources or systems into a single source or system. Consolidation of multiple data fields can take place after creating a data dictionary because the data dictionary can help the analyst to map and match the data fields from different sources or systems based on their definitions and descriptions, as well as eliminating any redundant or duplicate data fields.

asked 02/10/2024
Michael Bodine
33 questions

Which of the following techniques is used to quantify data?

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Which of the following is an example of structured data?

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Which of the following data cleansing issues will be fixed when a DISTINCT function is applied?

Missing data
Missing data
Duplicate data
Duplicate data
Redundant data
Redundant data
Invalid data
Invalid data
Suggested answer: B
Explanation:

This is because duplicate data refers to data that is repeated or copied in a data set, which can affect the quality and validity of the analysis. A DISTINCT function is a type of function that removes duplicate values from a column or a table, leaving only unique values. For example, a DISTINCT function in SQL that can achieve this is:

CompTIA DA0-001 image Question 33 explanation 95207 10022024175129000000

The other data cleansing issues will not be fixed by applying a DISTINCT function. Here is why:

Missing data refers to data that is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis. A DISTINCT function does not help with missing data, because it does not fill in or impute the missing values.

Redundant data refers to data that is unnecessary or irrelevant for the analysis, which can affect the efficiency and performance of the analysis. A DISTINCT function does not help with redundant data, because it does not remove or filter out the redundant values.

Invalid data refers to data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis. A DISTINCT function does not help with invalid data, because it does not validate or correct the invalid values.

asked 02/10/2024
Muhammad Imran
44 questions

Which of the following best describes an exploratory analysis?

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Five dogs have the following heights in millimeters:

300,430, 170, 470, 600 Which of the following is the standard deviation for the five dogs?

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A data analyst is designing a dashboard that will provide a story of sales and determine which site is providing the highest sales volume per customer The analyst must choose an appropriate chart to include in the dashboard. The following data is available:

CompTIA DA0-001 image Question 158 95332 10022024175130000000

Which of the following types of charts should be considered?

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The process of performing initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization is called:

a t-test.
a t-test.
a performance analysis.
a performance analysis.
an exploratory data analysis.
an exploratory data analysis.
a link analysis.
a link analysis.
Suggested answer: C
Explanation:

This is because exploratory data analysis is a type of process that performs initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization, such as box plots, histograms, scatter plots, etc. Exploratory data analysis can be used to understand and summarize the data, as well as to generate hypotheses or questions for further analysis or research. For example, exploratory data analysis can be used to identify and visualize the characteristics, features, or behaviors of the data, as well as to measure their distribution, frequency, or correlation. The other options are not types of processes that perform initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization. Here is what they mean:

A t-test is a type of statistical method that tests whether there is a significant difference between the means of two groups or samples, such as whether there is a difference between the average exam scores of two classes in this case. A t-test can be used to test or verify a claim or an assumption about the data, as well as to measure the confidence or the error of the estimation.

A performance analysis is a type of process that measures whether the data meets certain goals or objectives, such as targets, benchmarks, or standards. A performance analysis can be used to identify and visualize the gaps, deviations, or variations in the data, as well as to measure the efficiency, effectiveness, or quality of the outcomes. For example, a performance analysis can be used to determine if there is a gap between a student's test score and their expected score based on their previous performance.

A link analysis is a type of process that determines whether the data is connected to other datapoints, such as entities, events, or relationships. A link analysis can be used to identify and visualize the patterns, networks, or associations among the datapoints, as well as to measure the strength, direction, or frequency of the connections. For example, a link analysis can be used to determine if there is a connection between a customer's purchase history and their loyalty program status.

asked 02/10/2024
Eusebio Adrian
38 questions