CompTIA DA0-001 Online Practice Questions and Exam Preparation

CompTIA DA0-001 Online Questions & Answers

• Question 1:

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

  A. Missing data
  B. Duplicate data
  C. Redundant data
  D. Invalid data
  B. Duplicate data

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  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:

  

  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.

• Question 2:

  Which of the following is the best description of the term "data governance"?

  A. Data governance governs the development of a data visualization dashboard in an organization.
  B. Data governance is the policy that protects against data breaches by cybercriminals.
  C. Data governance is the process of analyzing, manipulating, and reporting data in an organization.
  D. Data governance is the availability, usability, integrity, and security of data in an enterprise.
  D. Data governance is the availability, usability, integrity, and security of data in an enterprise.

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  Explanation

  Data governance refers to the overarching management of data's availability, usability, integrity, and security within an organization. It involves setting policies and standards that govern data usage, determining data ownership, implementing

  data security measures, and ensuring that data is accessible for business insights while maintaining its quality. The goal of data governance is to ensure that data is consistent, trustworthy, and not misused, supporting compliance with data

  privacy regulations and enabling effective data analytics to optimize operations and drive business decision-making.

  References:

  Understanding Data Governance and Its Importance. The Role of Data Governance in Data Management. Defining Data Governance and Its Business Value.

• Question 3:

  You should always choose the analytics tool that is most appropriate for any given situation, even if that means acquiring a new tool.

  A. True.
  B. False.
  B. False.

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  Explanation

  The statement is false. You should not always choose the analytics tool that is most appropriate for any given situation, even if that means acquiring a new tool. Acquiring a new tool can be costly, time-consuming, and risky, as it may not be

  compatible with your existing data sources, systems, or processes. It may also require additional training, maintenance, and support. Therefore, you should always consider the trade-offs between the benefits and drawbacks of acquiring a

  new tool versus using an existing one. You should also evaluate the feasibility, availability, and reliability of the new tool before making a decision.

  CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

• Question 4:

  A data analyst must separate the column shown below into multiple columns for each component of the name:

  

  Which of the following data manipulation techniques should the analyst perform?

  A. Imputing
  B. Transposing
  C. Parsing
  D. Concatenating
  C. Parsing

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  Explanation

  Parsing is the data manipulation technique that should be used to separate the column into multiple columns for each component of the name. Parsing is the process of breaking down a string of text into smaller units, such as words, symbols, or numbers. Parsing can be used to extract specific information from a text column, such as names, addresses, phone numbers, etc. Parsing can also be used to split a text column into multiple columns based on a delimiter, such as a comma, space, or dash. In this case, the analyst can use parsing to split the column by the comma delimiter and create three new columns: one for the last name, one for the first name, and one for the middle initial. This will make the data more organized and easier to analyze.

• Question 5:

  An analyst collected data that includes primary account numbers, expiration dates, and service codes.

  Which of the following data governance classifications is used to describe this data?

  A. PI I
  B. PCI
  C. PBI
  D. PHI
  B. PCI

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  Explanation

• Question 6:

  Which of the following analysis techniques is an unsupervised data mining process?

  A. Clustering
  B. Descriptive
  C. Regression
  D. Predictive
  A. Clustering

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  Explanation

• Question 7:

  An analyst reviews the following table:

  

  Which of the following data types is represented in the values in the RefNo column?

  A. Numeric
  B. Real Number
  C. Currency
  D. Alphanumeric
  A. Numeric

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  Explanation

• Question 8:

  Given the following data table:

  

  Which of the following are appropriate reasons to undertake data cleansing? (Select two).

  A. Non-parametric data
  B. Missing data
  C. Duplicate data
  D. Invalid data
  E. Redundant data
  F. Normalized data
  B. Missing data
  D. Invalid data

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  Explanation

  Data cleansing is a critical process in data analytics to ensure the accuracy and quality of data. The reasons to undertake data cleansing include:

  Missing Data (B): Missing data can lead to incomplete analysis and biased results. It is essential to identify and address gaps in the dataset to maintain the integrity of the analysis. Invalid Data (D): Invalid data includes entries that are out of

  range, improperly formatted, or illogical (e.g., a negative age). Such data can corrupt analysis and lead to incorrect conclusions. Other options, such as non-parametric data (A), are not inherently errors but refer to a type of data that doesn't

  assume a normal distribution. Duplicate data and redundant data (E) could also be reasons for data cleansing, but they are not listed as options to select from in the provided image details. Normalized data (F) refers to data that has been

  processed to fit into a certain range or format and is typically not a reason for data cleansing.

  References:

  Understanding the importance of data quality and the impacts of missing and invalid data on research outcomes.

  Best practices in data cleansing.

  Data cleansing is required for various reasons, two of which are missing data (B) and invalid data (D). From the table provided, we can infer the necessity of cleansing in the context of ensuring data integrity and consistency. Missing data

  refers to the absence of data where it is expected, which can hinder analysis due to incomplete information. Invalid data refers to data that is incorrect, out of range, or in an inappropriate format, which can lead to inaccuracies in any analysis

  or report. Both these issues can significantly affect the outcomes of any data-related operations and thus need to be rectified through the data cleansing process.

• Question 9:

  A data analyst has removed the outliers from a data set due to large variances. Which of the following central tendencies would be the best measure to use?

  A. Range
  B. Mean
  C. Mode
  D. Median
  D. Median

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  Explanation

  The median is recognized as the most appropriate measure of central tendency when outliers have been removed from a dataset. This is because the median is less influenced by extreme values compared to the mean. When outliers are

  present, they can significantly skew the mean, making it an unreliable measure of central tendency. The median, on the other hand, is the middle value of a dataset when ordered from least to greatest and remains unaffected by the extremes.

  Therefore, it provides a better representation of the central location of the data after outliers have been excluded.

  References:

  Guidelines for Removing and Handling Outliers in Data.

  Mean, Median, and Mode: Measures of Central Tendency.

  Which measure of central tendency should be used when there is an outlier?

  How are measures of central tendency affected by outliers?

• Question 10:

  A data analyst has a set with more than 40.000 rows in the sample schema below:

  

  The analyst would like to create one column that contains the customers' birth dates.

  Which of the following data quality dimensions would BEST explain the reason for compilation?

  A. Data accuracy
  B. Data completeness
  C. Data duplication
  D. Data integrity
  D. Data integrity

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  Explanation

  Data integrity is the dimension that measures the consistency and validity of data across different data sources. In this case, the data analyst wants to create one column that contains the customers' birth dates, but the data is stored in

  different formats and locations in the sample schema. For example, some customers have their birth dates in the customer table, while others have their birth years in the sales table. To compile the data into one column, the data analyst

  needs to ensure that the data is consistent and valid across the tables. Therefore, data integrity is the best explanation for the reason for compilation.

  References:

  Data Quality Dimensions - DATAVERSITY, The 6 Data Quality Dimensions with Examples | Collibra