Achive your Success with Latest CompTIA DA0-001 Exam [Dec 09, 2023]
The DA0-001 Exam Test For Brief Preparation
NEW QUESTION # 102
A sales team wants visibility of current sales numbers, pipeline, and team performance. The team would also like to see calculations of individuals' earned commissions and projected commissions based on sales, but they want that information to be kept confidential. Which of the following would be the BEST way to provide this visibility?
- A. Create a dashboard displaying a data refresh date so users know the current sales numbers and configure permissions to control access.
- B. Create a dashboard for sales numbers, pipeline, and team and individual performance for the management team.
- C. Create a dashboard with views for team, individuals, and management. Configure permissions to control access.
- D. Create a dashboard with filters for the overall team, individuals, and management. Users can filter to see the data they want.
Answer: B
NEW QUESTION # 103
An analysts building a monthly report for production and wants to ensure the audience is aware of its once-a-month cadence. Which of the following is the MOST important to convey that information?
- A. Frequently asked questions
- B. The date of the dashboard build
- C. A report summary
- D. The data refresh date
Answer: B
NEW QUESTION # 104
Andrew conducts a study and wants to capture eye color.
What kind of data is eye color?
Choose the best response.
- A. Discrete.
- B. Alphanumeric.
- C. Categorical.
- D. Continuous.
Answer: C
Explanation:
Correct answer B. Categorical.
Eye color can only fall into a certain range of values; as such, it is categorical.
NEW QUESTION # 105
A data analyst wants to create "Income Categories" that would be calculated based on the existing variable "Income". The "Income Categories" would be as follows:
Income category 1: less than $1.
Income category 2: more than $1 and less than $20,000.
Income category 3: more than $20,001 and less than $40,000.
Income category 4: more than $40,001.
Which of the following data manipulation techniques should the data analyst use to create "Income Categories"?
- A. Derived variables
- B. Data merge
- C. Data blending
- D. Data append
Answer: A
Explanation:
The correct answer is B: Derived variables Derived variables are variables that you create by calculating or categorizing variables that already exist in your data set.
Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set. Data blending is incorrect.
Data blending involves pulling data from different sources and creating a single, unique, dataset for visualization and analysis.
Data append is incorrect. A data append is a process that involves adding new data elements to an existing database.
NEW QUESTION # 106
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. Mean
- B. Standard deviation
- C. Median
- D. Mode
Answer: C
Explanation:
Explanation
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.
NEW QUESTION # 107
Given the diagram below:
Which of the following data schemas shown?
- A. Data Lake
- B. Relational database
- C. Key-value pairs
- D. Online transactional processing
Answer: B
Explanation:
Explanation
A relational database is a type of database that organizes data into tables, where each table has a fixed number of columns and a variable number of rows. Each row in a table represents a record or an entity, and each column represents an attribute or a property of that entity. The tables are linked by common fields, called keys, which enable the database to establish relationships between the data. A relational database schema is a diagram that shows the structure and organization of the tables, columns, keys, and constraints in a relational database. The diagram given in the question is an example of a relational database schema, as it shows two tables: "Runs" and "Experiments", with their respective columns, data types, and primary keys. The "Runs" table also has a foreign key that references the "ExperimentId" column in the "Experiments" table, indicating a relationship between the two tables. Therefore, the correct answer is D. References: What is a database schema? | IBM, Database Schema - Javatpoint
NEW QUESTION # 108
Which of the following is used for calculations and pivot tables?
- A. SAS
- B. Microsoft Excel
- C. IBM SPSS
- D. Domo
Answer: B
NEW QUESTION # 109
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 initial analysis and use a Pareto chart.
- B. Conduct a trend analysis and use a scatter chart.
- C. Conduct a link analysis and illustrate the connection points.
- D. Conduct an exploratory analysis and use descriptive statistics.
Answer: D
NEW QUESTION # 110
A data set was recorded using multimedia technology. Which of the following is a necessary step on the way to interpretation?
- A. Transcription
- B. Sequential analysis
- C. Structural equation modeling
- D. Sampling
Answer: A
Explanation:
Explanation
The correct answer is B. Transcription.
Transcription is a necessary step on the way to interpretation when a data set was recorded using multimedia technology. Multimedia technology refers to the use of various forms of media, such as audio, video, images, and text, to capture and present information1 Transcription is the process of converting multimedia data into written or textual form, which can then be analyzed using various methods and tools2 Transcription can help to make the data more accessible, searchable, and manageable, as well as to preserve the data for future use.
Structural equation modeling is not correct, because it is a statistical technique that tests the causal relationships between multiple variables using observed and latent variables. Structural equation modeling is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.
Sequential analysis is not correct, because it is a method of analyzing the order and timing of events or behaviors in a data set. Sequential analysis is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.
Sampling is not correct, because it is the process of selecting a subset of data from a larger population for analysis. Sampling is not a necessary step on the way to interpretation, but rather a preliminary step that can be done before collecting or analyzing the data.
NEW QUESTION # 111
What type of data is best suited for display using a tree map?
- A. Nested data
- B. Time series data
- C. Text data
- D. Numeric data
Answer: A
NEW QUESTION # 112
Given the table below:
Which of the following variable types BEST describes the "Year" column?
- A. Alphanumeric
- B. Text
- C. Numeric
- D. Date
Answer: D
Explanation:
Explanation
This is because date is a type of variable that represents a specific point or period in time, such as a day, a month, or a year. Date variables can be used to store, manipulate, or analyze temporal data, such as transaction dates, birth dates, or expiration dates. For example, date variables can be used to calculate the duration or the difference between two dates, or to filter or sort the data by date. The other variable types are not correct descriptions of the "Year" column. Here is why:
Numeric is a type of variable that represents a numerical value, such as an integer, a decimal, or a fraction. Numeric variables can be used to store, manipulate, or analyze quantitative data, such as amounts, prices, or scores. For example, numeric variables can be used to perform arithmetic operations or calculations on the data, or to measure the central tendency or the dispersion of the data.
Alphanumeric is a type of variable that represents a combination of alphabetic and numeric characters, such as letters, numbers, symbols, or spaces. Alphanumeric variables can be used to store, manipulate, or analyze textual data, such as names, addresses, or codes. For example, alphanumeric variables can be used to concatenate or split the data, or to search or match the data using patterns or expressions.
Text is a type of variable that represents a sequence of alphabetic characters, such as letters or words.
Text variables can be used to store, manipulate, or analyze textual data, such as names, categories, or labels. For example, text variables can be used to change the case or the length of the data, or to compare or classify the data using criteria or rules.
NEW QUESTION # 113
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. Transposing
- B. Concatenating
- C. Imputing
- D. Parsing
Answer: D
NEW QUESTION # 114
Consider the following dataset which contains information about houses that are for sale:
Which of the following string manipulation commands will combine the address and region name columns to create a full address?
full_address------------------------- 85 Turner St, Northern Metropolitan 25 Bloomburg St, Northern Metropolitan 5 Charles St, Northern Metropolitan 40 Federation La, Northern Metropolitan 55a Park St, Northern Metropolitan
- A. SELECT CONCAT(regionname, ' , ' , address) AS full_address FROM melb LIMIT 5
- B. SELECT CONCAT(address, ' , ' , regionname) AS full_address FROM melb LIMIT 5;
- C. SELECT CONCAT(regionname, '-' , address) AS full_address FROM melb LIMIT 5;
- D. SELECT CONCAT(address, '-' , regionname) AS full_address FROM melb LIMIT 5;
Answer: B
Explanation:
The correct answer is A: SELECT CONCAT(address, ' , ' , regionname) AS full_address FROM melb LIMIT 5; String manipulation (or string handling) is the process of changing, parsing, splicing, pasting, or analyzing strings. SQL is used for managing data in a relational database. The CONCAT() function adds two or more strings together. Syntax CONCAT(stringl, string2,... string_n) Parameter Values Parameter Description stringl, string2, string_n Required. The strings to add together.
NEW QUESTION # 115
An analyst is preparing a report that contains weather data. The temperatures are shown in Fahrenheit. but they must be reported in Celsius. Which of the following should the analyst do to fix this issue?
- A. Aggregate the data.
- B. Standardize the data.
- C. Rescale the data.
- D. Normalize the data.
Answer: C
Explanation:
Explanation
The analyst should rescale the data to fix this issue. Rescaling is a process of transforming data from one scale to another, such as changing the units of measurement. In this case, the analyst needs to rescale the temperatures from Fahrenheit to Celsius, which are two different scales for measuring temperature. To do this, the analyst can use the following formula:
Celsius = (Fahrenheit - 32) * 5/9
This formula converts each temperature value from Fahrenheit to Celsius by subtracting 32 and multiplying by
5/9. For example, if the temperature is 68°F, the rescaled value in Celsius is:
Celsius = (68 - 32) * 5/9 Celsius = 20°C
Rescaling the data can help the analyst to report the temperatures in a consistent and accurate way, and to avoid any confusion or errors that may arise from using different scales. Rescaling can also make the data more comparable and compatible with other data sources or standards that use the same scale12.
NEW QUESTION # 116
A data analyst is developing a data dictionary that aligns with a company's data management processes and policies. Which of the following best describes what should be included in the data dictionary?
- A. Information explaining the business methodologies
- B. Information containing the links to business data
- C. Information containing definitions of the business data
- D. Information describing the data analysis phases
Answer: C
NEW QUESTION # 117
An analyst modified a data set that had a number of issues. Given the original and modified versions:
Which of the following data manipulation techniques did the analyst use?
- A. Parsing
- B. Deriving
- C. Imputation
- D. Recoding
Answer: D
Explanation:
Explanation
The correct answer is B. Recoding.
Recoding is a data manipulation technique that involves changing the values or categories of a variable to make it more suitable for analysis. Recoding can be used to simplify or group the data, to correct errors or inconsistencies, or to create new variables from existing ones12 In the example, the analyst used recoding to change the values of Var001, Var002, Var003, and Var004 from numerical to textual form. The analyst also used recoding to assign meaningful labels to the values, such as
"Absent" for 0, "Present" for 1, "Low" for 2, "Medium" for 3, and "High" for 4. This makes the data more understandable and easier to analyze.
NEW QUESTION # 118
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