CompTIA CompTIA Data+ DA0-001 Questions & Answers

• Question 31:

  An analyst needs to conduct a quick analysis. Which of the following is the FIRST step the analyst should perform with the data?

  A. Conduct an exploratory analysis and use descriptive statistics.

  B. Conduct a trend analysis and use a scatter chart.

  C. Conduct a link analysis and illustrate the connection points.

  D. Conduct an initial analysis and use a Pareto chart.

  Correct Answer: A

  Explanation: The first step the analyst should perform with the data is to conduct an exploratory analysis and use descriptive statistics. Exploratory analysis is a type of analysis that aims to summarize the main characteristics of the data, identify patterns, outliers, and relationships, and generate hypotheses for further investigation. Descriptive statistics are numerical measures that describe the central tendency, variability, and distribution of the data, such as mean, median, mode, standard deviation, range, quartiles, etc. Exploratory analysis and descriptive statistics can help the analyst gain a better understanding of the data and its quality, as well as prepare the data for further analysis.

• Question 32:

  Which one of the following programming languages is specifically designed for use in analytics applications?

  A. Python.

  B. R

  C. C++

  D. Java.

  Correct Answer: B

• Question 33:

  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.

  Correct Answer: B

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

• Question 34:

  Which of the following is a difference between a primary key and a unique key?

  A. A unique key cannot take null values, whereas a primary key can take null values.

  B. There can be only one primary key in a data set, whereas there can be multiple unique keys.

  C. A primary key can take a value more than once, whereas a unique key cannot take a value more than once.

  D. A primary key cannot be a date variable, whereas a unique key can be.

  Correct Answer: B

  The correct answer is B. There can be only one primary key in a data set, whereas there can be multiple unique keys. A primary key is a column or a set of columns that uniquely identifies each row in a table. A table can have only one primary key, which also enforces the NOT NULL constraint on the column(s) involved. A primary key can also be referenced by a foreign key of another table to establish a relationship between the tables12 A unique key is a column or a set of columns that also uniquely identifies each row in a table, but it is not the primary key. A table can have more than one unique key, which also allows one NULL value for the column(s) involved. A unique key can also be referenced by a foreign key of another table to establish a relationship between the tables12 Some of the differences between a primary key and a unique key are: A primary key creates a clustered index on the column(s), whereas a unique key creates a non-clustered index on the column(s)3 A primary key does not allow any NULL values, whereas a unique key allows one NULL value for the column(s)123 A primary key can be a unique key, but a unique key cannot be a primary key12

• Question 35:

  Given the below:

  

  Which of the following numbers represents a Type I error?

  A. 1

  B. 2

  C. 3

  D. 4

  Correct Answer: C

• Question 36:

  Which of the following would a data analyst look for first if 100% participation is needed on survey results?

  A. Missing data

  B. Invalid data

  C. Redundant data

  D. Duplicate data

  Correct Answer: A

  Missing data is a type of data quality issue that occurs when some values in a data set are not recorded or available. Missing data can affect the validity and reliability of survey results, especially if the missing values are not random or ignorable. Missing data can also reduce the sample size and the statistical power of the analysis12 If 100% participation is needed on survey results, a data analyst would look for missing data first, because missing data would indicate that some participants did not complete or submit the survey, or that some responses were not recorded or transmitted correctly. A data analyst would need to identify the causes and patterns of missing data, and apply appropriate methods to handle or prevent missing data, such as imputation, deletion, weighting, or follow-up12

• Question 37:

  Which of the following is the first step an analyst should perform upon receiving a business request for analysis?

  A. Determine the data needs and sources for analysis.

  B. Initiate the analysis for exploratory data analysis.

  C. Review the business questions to understand the scope.

  D. Finalize the methodology to solve the problem.

  Correct Answer: C

  Explanation: Answer: C. Review the business questions to understand the scope. The first step an analyst should perform upon receiving a business request for analysis is to review the business questions to understand the scope of the problem, the objectives, and the expected outcomes. This will help the analyst to define the analytical approach, identify the data needs and sources, and plan the analysis process. Reviewing the business questions will also help the analyst to communicate with the stakeholders and clarify any assumptions or ambiguities1. Option A is incorrect, as determining the data needs and sources for analysis is not the first step, but rather a subsequent step that depends on the business questions and the analytical approach. Option B is incorrect, as initiating the analysis for exploratory data analysis is not the first step, but rather a part of the analysis process that involves examining and summarizing the data, identifying patterns and outliers, and testing hypotheses. Option D is incorrect, as finalizing the methodology to solve the problem is not the first step, but rather a later step that involves selecting and applying the appropriate analytical techniques, tools, and models to answer the business questions.

• Question 38:

  A data analyst needs to perform a full outer join of a customer's orders using the tables below:

  

  Which of the following is the mean of the order quantity?

  A. 73.5

  B. 76.5

  C. 78.8

  D. 81.5

  Correct Answer: D

  The correct answer is D. OUTER JOIN, seven rows.

  An OUTER JOIN is a type of SQL join that returns all the rows from both tables, regardless of whether there is a match or not. If there is no match, the missing side will have null values. An OUTER JOIN can be either a LEFT JOIN, a RIGHT

  JOIN, or a FULL JOIN, depending on which table's rows are preserved1

  Using the example tables, a FULL OUTER JOIN query would look like this:

  SELECT Cust_id, Order_id, Order_qty FROM Sales_table FULL OUTER JOIN Order_table ON Sales_table.Order_id = Order_table.Order_id;

  The result of this query would be:

  Cust_id | Order_id | Order_qty --------?--------?-------- 1 | 1 | 100 2 | 2 | 50 3 | 3 | 25 4 | 4 | 75 NULL | 5 | 10 NULL | 6 | 20 NULL | 7 | 15

  As you can see, the query returns seven rows, one for each order in either table. The orders that are not in the Sales_table have null values for the Cust_id column. To find the mean of the order quantity, we need to sum up the order

  quantities and divide by the number of rows. In this case, the mean is (100 + 50 + 25 + 75 + 10 + 20 + 15) / 7 = 42.14. Rounding to one decimal place, we get 42.1 as the mean of the order quantity.

• Question 39:

  An analyst is working with the income data of suburban families in the United States. The data set has a lot of outliers, and the analyst needs to provide a measure that represents the typical income. Which of the following would BEST fulfill the analyst's goal?

  A. Median

  B. Mean

  C. Mode

  D. Standard deviation

  Correct Answer: A

  his is because median is a type of statistical measure that represents the typical value or central tendency of a data set, which means that it divides the data set into two equal halves, such that half of the values are above it and half are below it. Median can be used to provide a measure that represents the typical income of suburban families in the United States, especially when the data set has a lot of outliers, which means that it has values that are unusually high or low compared to the rest of the data set. Median can provide a measure that represents the typical income of suburban families in the United States, because it is not affected or skewed by the outliers, as it only depends on the middle value or the middle two values of the data set, regardless of how extreme or distant the outliers are. For example, median can provide a measure that represents the typical income of suburban families in the United States, by finding the income value that splits the data set into two equal groups of families, such that 50% of the families have higher incomes and 50% have lower incomes. The other statistical measures are not the best measures to represent the typical income of suburban families in the United States. Here is why:

  Mean is a type of statistical measure that represents the average value or central tendency of a data set, which means that it is the sum of all the values divided by the number of values. Mean is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is affected or skewed by the outliers, as it takes into account all the values in the data set, regardless of how extreme or distant they are. For example, mean can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is influenced by a few very high or very low incomes, which could make it higher or lower than most of the incomes in the data set. Mode is a type of statistical measure that represents the most frequent value or mode of a data set, which means that it is the value that occurs most often in the data set. Mode is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is not representative or indicative of the central tendency or distribution of the data set, as it only depends on the count or occurrence of a single value or a few values in the data set, regardless of how common or rare they are. For example, mode can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is repeated more often than others, which could be an outlier or an anomaly in the data set. Standard deviation is a type of statistical measure that represents the amount of dispersion or variation of a data set, which means that it quantifies how much the values in a data set vary or deviate from the mean or average of the data set. Standard deviation is not a measure that represents the typical income of suburban families in the United States, but rather a measure that describes the spread or distribution of their incomes, as well as identifies any outliers or extreme values in their incomes. For example, standard deviation can provide a measure that describes how diverse or homogeneous their incomes are, as well as how far their incomes are from their average income.

• Question 40:

  A data analyst is asked on the morning of April 9, 2020, to create a sales report that identifies sales year to date. The daily sales data is current through the end of the day. Which of the following date ranges should be on the report?

  A. January 1, 2020 to April 1, 2020

  B. January 1, 2020 to April 7, 2020

  C. January 1, 2020 to April 8, 2020

  D. January 1, 2020 to April 9, 2020

  Correct Answer: D

  This is because sales year to date refers to the sales that have occurred from the beginning of the current year until the current date. By creating a sales report that identifies sales year to date, the analyst can measure and compare the sales performance and progress of the current year. Since the analyst is asked to create the sales report on the morning of April 9, 2020, and the daily sales data is current through the end of the day, the date range that should be on the report is January 1, 2020 to April 9, 2020. The other date ranges are not correct for identifying sales year to date. Here is why:

  January 1, 2020 to April 1, 2020 would not include the sales that occurred in the first eight days of April, which would underestimate the sales year to date. January 1, 2020 to April 7, 2020 would not include the sales that occurred in the last two days of April, which would also underestimate the sales year to date. January 1, 2020 to April 8, 2020 would not include the sales that occurred on April 9, which would also underestimate the sales year to date.