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質問 # 217
Analytics reports should follow corporate style guidelines.
- A. True.
- B. False.
正解：A
質問 # 218
Olivia has 15 people on her data analytics team. Her team's charter requires that all team members have read access to the finance, human resources, sales, and customer service areas of the corporate data warehouse.
What is the best way to provision access to her team?
Choose the best answer.
- A. Since there are four discrete data subjects, create one role for each subject area.
- B. Create a single role that includes finance, human resources, sales, and customer services data.
- C. Enable multifactor authentication (MFA) to protect the data.
- D. Since there are 15 people on her team, create a role for each person to improve security.
正解：B
解説：
Correct answer D. Create a single role that includes finance, human resources, sales, and customer services data.
While MFA is a good security practice, it doesn't govern access to data.
Creating a single role for her team and assigning that role to the individuals on the team is the best approach.
質問 # 219
A column is being used to store strings of variable lengths. Performance is a concern, so the column needs to use as little space as possible. Which of the following data types best meets these requirements?
- A. char
- B. nchar
- C. varchar
- D. nvarchar
正解：C
解説：
VARCHAR is a data type optimized for storing variable-length strings. It only uses the necessary space for each value, making it more efficient in terms of storage compared to CHAR, which allocates a fixed amount of space regardless of the string's actual length.
質問 # 220
A data analyst has been asked to create a daily manufacturing report for the floor manager Which of the following metrics should be included in the report?
- A. Daily corporate employee count
- B. Annual sales budget
- C. End-of-day stock price
- D. Tons of steel produced per hour
正解：D
質問 # 221
Standardized tests are given to students in the middle of each month, and the results are ready by the end of the month. The superintendent needs a quick view of test performance. Which of the following would be the best recommendation to meet the superintendent's requirements?
- A. A report of test scores by classroom, emailed to the superintendent at the end of the month
- B. A dashboard with a continuous data stream and saved searches
- C. A report of test scores with pie charts showing student performance
- D. A dashboard with a scheduled delivery, the ability to filter scores by school, and bar charts for comparison
正解：D
解説：
A dashboard with a scheduled delivery is an efficient way to provide a quick view of test performance. It allows for timely updates, which is crucial given that the superintendent needs the information promptly at the end of each month. The ability to filter scores by school enables the superintendent to easily segment and analyze the data as needed. Bar charts are effective for comparison and can visually communicate the performance across different schools or other categories, making it easier to identify trends and outliers at a glance.
References:
* Best practices in data visualization recommend using dashboards for real-time data monitoring and quick access to key metrics1.
* Guidelines for presenting performance data suggest that visual tools like bar charts are helpful in comparing and analyzing data effectively1.
* Educational performance data analysis often involves comparing scores across different schools or classrooms, which is facilitated by a well-designed dashboard2.
質問 # 222
A sales director has requested a report for individual team members within the division be developed. The director would like the report to be shared with all team members, but individual team members should not be identifiable within the report Which of the following access requirements would support the director's needs?
- A. Create an acceptable use policy for the sales data.
- B. Release the report as user-group-based access and include data masking.
- C. Get a data use agreement from the individual team members.
- D. Provide the report based on role and include data encryption.
正解：B
質問 # 223
A data analyst needs to create a data visualization that aids in un the cumulative impact of sequentially introduced values that are positive or negative. Which of the following data visualization methods should the analyst use?
- A. A line chart
- B. A scatter plot
- C. A bubble chart
- D. A waterfall chart
正解：D
解説：
A waterfall chart is a type of data visualization that shows the cumulative impact of sequentially introduced values that are positive or negative. A waterfall chart typically has an initial value and a final value, with intermediate values shown as floating columns that either add to or subtract from the initial value. A waterfall chart can help visualize how different factors contribute to a net change in a value over time. Therefore, the correct answer is B. References: [Waterfall Chart | Definition & Examples - Investopedia], [Waterfall Charts in Excel | How to Create Waterfall Chart in Excel?]
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質問 # 224
Given the following data tables:
Which of the following MDM processes needs to take place FIRST?
- A. Compliance with regulations
- B. Standardization of data field names
- C. Consolidation of multiple data fields
- D. Creation of a data dictionary
正解：D
解説：
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.
質問 # 225
An analyst is preparing a report that contains weather dat
a. 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. Rescale the data.
- B. Aggregate the data.
- C. Normalize the data.
- D. Standardize the data.
正解：A
解説：
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.
質問 # 226
Which of the following is a relational database?
- A. JSON
- B. NoSQL
- C. Excel
- D. SQL
正解：D
質問 # 227
Which of the following data types is best for representing count data?
- A. Continuous
- B. Sequential
- C. Referential
- D. Discrete
正解：D
解説：
Comprehensive and Detailed In-Depth
Count data refers to data that represents the number of occurrences of an event or the number of items in a set, which are whole numbers (integers). Understanding the nature of data types is crucial for accurate data analysis and representation.
Discrete Data: This type of data consists of distinct, separate values. Discrete data is countable and often represents items that can be counted in whole numbers, such as the number of customers, defects, or occurrences. Since count data involves whole numbers, discrete data is the most appropriate representation.
Referential Data: This pertains to data that establishes relationships between tables in a database, often using keys. It is not related to counting occurrences.
Sequential Data: This involves data that follows a specific order or sequence, such as timestamps or ordered events. While it indicates order, it doesn't inherently represent count data.
Continuous Data: This type of data can take any value within a range and is measurable rather than countable, such as height, weight, or temperature. Continuous data is not suitable for representing count data, as counts are discrete by nature.
Therefore, Discrete data is the best choice for representing count data, as it accurately reflects whole number counts of occurrences or items.
質問 # 228
Refer to the exhibit.
A customer list from a financial services company is shown below:
A data analyst wants to create a likely-to-buy score on a scale from 0 to 100, based on an average of the three numerical variables: number of credit cards, age, and income. Which of the following should the analyst do to the variables to ensure they all have the same weight in the score calculation?
- A. Normalize the variables.
- B. Calculate the percentiles of the variables.
- C. Calculate the standard deviations of the variables.
- D. Recode the variables.
正解：A
解説：
Normalizing the variables means scaling them to a common range, such as 0 to 1 or -1 to 1, so that they have the same weight in the score calculation. Recoding the variables means changing their values or categories, which would alter their meaning and distribution. Calculating the percentiles of the variables means ranking them relative to each other, which would not account for their actual magnitudes. Calculating the standard deviations of the variables means measuring their variability, which would not make them comparable. Reference: CompTIA Data+ Certification Exam Objectives, page 10
質問 # 229
Which of the following is an example of PII?
- A. Gender
- B. Age
- C. Ethnicity
- D. Name
正解：D
解説：
Explanation
A name is an example of personally identifiable information (PII), which is any data that can be used to identify someone, either on its own or with other relevant data. A name is a direct identifier, which means that it can uniquely identify a person without the need for any additional information. For example, a full name, such as John Smith, can be used to distinguish or trace an individual's identity1.
Other examples of direct identifiers include:
Social Security Number
Passport number
Driver's license number
Email address
Phone number
質問 # 230
An e-commerce company recently tested a new website layout. The website was tested by a test group of customers, and an old website was presented to a control group. The table below shows the percentage of users in each group who made purchases on the websites:
Which of the following conclusions is accurate at a 95% confidence interval?
- A. In Germany, the increase in conversion from the new layout was not significant.
- B. In general, users who visit the new website are more likely to make a purchase.
- C. The new layout has the lowest conversion rates in the United Kingdom.
- D. In France, the increase in conversion from the new layout was not significant.
正解：B
解説：
Explanation
The conclusion that is accurate at a 95% confidence interval is that in general, users who visit the new website are more likely to make a purchase. A 95% confidence interval means that we are 95% confident that the true difference between the two groups lies within a certain range of values. To calculate the 95% confidence interval, we can use the following formula:
CI = (p1 - p2) ± 1.96 * sqrt(p * (1 - p) * (1/n1 + 1/n2))
where p1 and p2 are the conversion rates for the test and control groups, respectively, p is the pooled conversion rate, n1 and n2 are the sample sizes for the test and control groups, respectively, and 1.96 is the z-score for a 95% confidence level.
Using this formula, we can calculate the 95% confidence interval for each country as follows:
Country | p1 | p2 | n1 | n2 | p | CI United States | 0.12 | 0.11 | 2000 | 2000 | 0.115 | (-0.006, 0.026) Germany |
0.06 | 0.04 | 1000 | 1000 | 0.05 | (-0.002, 0.042) United Kingdom | 0.09 | 0.07 | 1500 | 1500 | 0.08 | (-0.003,
0.053) France | 0.08 | 0.08 | 1200 | 1200 | 0.08 | (-0.024, 0.024) Canada | 0.05 | 0.03 | 800 | 800 | 0.04 | (-0.005,
0.045)
We can see that for all countries except France, the confidence interval does not include zero, which means that the difference between the test and control groups is statistically significant at a 95% confidence level.
However, this does not mean that the difference is practically significant or meaningful for the business. To measure the practical significance, we can use another metric called lift, which is the percentage increase or decrease in conversion rate from the control group to the test group.
Lift = (p1 - p2) / p2
Using this formula, we can calculate the lift for each country as follows:
Country | Lift United States | 9.09% Germany | 50% United Kingdom |28.57% France|0% Canada|66.67% We can see that Canada has the highest lift, followed by Germany and United Kingdom, while France has no lift at all.
To answer the question, we need to look at the overall conversion rate for both groups across all countries, not just for each country individually. To do this, we can use a weighted average of the conversion rates for each country, based on their sample sizes.
Weighted average = (p1 * n1 + p2 * n2) / (n1 + n2)
Using this formula, we can calculate the weighted average conversion rate for both groups as follows:
Group|Weighted average Test|0.084 Control|0.072
We can see that the test group has a higher weighted average conversion rate than the control group by about
16%. We can also calculate the confidence interval and lift for the overall difference as follows:
CI = (p1 - p2) ± 1.96 * sqrt(p * (1 - p) * (1/n1 + 1/n2)) = (0.084 - 0.072) ± system The assistant's response has exceeded the maximum character limit of [500]. Please shorten your response or split it into multiple messages.
質問 # 231
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. Data append
- B. Data blending
- C. Derived variables
- D. Data merge
正解：C
解説：
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.
質問 # 232
A business intelligence engineer needs to reduce the size of a data model for reporting purposes. The data set contains more than one million rows, and the table has a date-time column named Date. Which of the following should the analyst do to complete this task?
- A. Split the Date column into two columns-time and date.
- B. Trim the date.
- C. Round the hour of the Date column to the start of the hour.
- D. Change the data type of the Date column to text.
正解：C
解説：
Comprehensive and Detailed In-Depth
When dealing with large datasets, optimizing data storage is crucial for performance and efficiency. The Date column, containing date-time values, can be optimized by reducing its precision, thereby decreasing the storage requirements.
Changing the data type to text: Converting the Date column to text would likely increase the storage size and complicate date-time operations, as text representations are less efficient for date-time computations.
Trimming the date: This option is ambiguous. If it refers to removing time components, it could lead to loss of essential information.
Rounding the hour to the start of the hour: By adjusting the time to the top of the hour (e.g., 10:45 becomes 10:00), the precision is reduced, which can decrease the storage size and improve performance without significant loss of essential information.
Splitting into two columns-time and date: This approach increases the number of columns and may not effectively reduce the overall data size.
Therefore, rounding the hour to the start of the hour is the most effective method to reduce the data model's size while preserving essential temporal information.
質問 # 233
What European law requires that organizations handling personal information designate a Data Protection Officer (DPO)?
- A. GLBA (Gramm-Leach-Bliley Act)
- B. FERPA (Family Educational Rights and Privacy Act)
- C. GDPR (General Data Protection Regulation)
- D. HIPAA (Health Insurance Portability and Accountability Act)
正解：C
解説：
The General Data Protection Regulation 2016/679 is a regulation in EU law on data protection and privacy in the European Union and the European Economic Area.
質問 # 234
The current date is July 14, 2020. A data analyst has been asked to create a report that shows the company's year-over-year Q2 2020 sales. Which of the following reports should the analyst compare?
- A. Q2 2020 and Q2 2021
- B. YTD 2020 and YTD 2019
- C. Q2 2020 and Q4 2019
- D. Q2 2020 and Q2 2019
正解：D
質問 # 235
While reviewing survey data, an analyst notices respondents entered "Jan," "January," and "01" as responses for the month of January. Which of the following steps should be taken to ensure data consistency?
- A. Sort any of the responses that say "Jan" and update them to "01".
- B. Replace any of the responses that have "01".
- C. Delete any of the responses that do not have "January" written out.
- D. Filter on any of the responses that do not say "January" and update them to "January".
正解：D
解説：
Explanation
Filter on any of the responses that do not say "January" and update them to "January". This is because filtering and updating are data cleansing techniques that can be used to ensure data consistency, which means that the data is uniform and follows a standard format. By filtering on any of the responses that do not say "January" and updating them to "January", the analyst can make sure that all the responses for the month of January are written in the same way. The other steps are not appropriate for ensuring data consistency. Here is why:
Deleting any of the responses that do not have "January" written out would result in data loss, which means that some information would be missing from the data set. This could affect the accuracy and reliability of the analysis.
Replacing any of the responses that have "01" would not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: "Jan" and "January". This could cause confusion and errors in the analysis.
Sorting any of the responses that say "Jan" and updating them to "01" would also not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: "01" and
"January". This could also cause confusion and errors in the analysis.
質問 # 236
Given the following data table:
Which of the following are appropriate reasons to undertake data cleansing? (Select two).
- A. Duplicate data
- B. Invalid data
- C. Missing data
- D. Normalized data
- E. Non-parametric data
- F. Redundant data
正解：B、F
質問 # 237
Which of the following should be accomplished NEXT after understanding a business requirement for a data analysis report?
- A. Rephrase the business requirement.
- B. Build a mock dashboard/presentation layout.
- C. Perform exploratory data analysis.
- D. Determine the data necessary for the analysis
正解：D
解説：
The next step after understanding a business requirement for a data analysis report is to determine the data necessary for the analysis. This step involves identifying the data sources, variables, metrics, and dimensions that are relevant and sufficient to answer the business question or problem. This step also involves assessing the availability, quality, and accessibility of the data, and planning how to collect, clean, and prepare the data for analysis. The other options are not the next steps after understanding a business requirement, but rather subsequent steps in the data analysis process. Rephrasing the business requirement is a step that can help clarify and refine the business question or problem before determining the data necessary for the analysis. Building a mock dashboard/presentation layout is a step that can help design and visualize the report before performing the data analysis. Performing exploratory data analysis is a step that can help explore and summarize the data before drawing conclusions and recommendations from the data. Reference: Data Analysis Process - DataCamp
質問 # 238
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. Concatenating
- B. Parsing
- C. Transposing
- D. Imputing
正解：B
解説：
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 dash1. 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.
質問 # 239
An e-commerce company recently tested a new website layout. The website was tested by a test group of customers, and an old website was presented to a control group. The table below shows the percentage of users in each group who made purchases on the websites:
Which of the following conclusions is accurate at a 95% confidence interval?
- A. In Germany, the increase in conversion from the new layout was not significant.
- B. In general, users who visit the new website are more likely to make a purchase.
- C. The new layout has the lowest conversion rates in the United Kingdom.
- D. In France, the increase in conversion from the new layout was not significant.
正解：B
解説：
The conclusion that is accurate at a 95% confidence interval is that in general, users who visit the new website are more likely to make a purchase. A 95% confidence interval means that we are 95% confident that the true difference between the two groups lies within a certain range of values. To calculate the 95% confidence interval, we can use the following formula:
CI = (p1 - p2) ± 1.96 * sqrt(p * (1 - p) * (1/n1 + 1/n2))
where p1 and p2 are the conversion rates for the test and control groups, respectively, p is the pooled conversion rate, n1 and n2 are the sample sizes for the test and control groups, respectively, and 1.96 is the z-score for a 95% confidence level.
Using this formula, we can calculate the 95% confidence interval for each country as follows:
Country | p1 | p2 | n1 | n2 | p | CI United States | 0.12 | 0.11 | 2000 | 2000 | 0.115 | (-0.006, 0.026) Germany | 0.06 | 0.04 | 1000 | 1000 | 0.05 | (-0.002, 0.042) United Kingdom | 0.09 | 0.07 | 1500 | 1500 | 0.08 | (-0.003, 0.053) France | 0.08 | 0.08 | 1200 | 1200 | 0.08 | (-0.024, 0.024) Canada | 0.05 | 0.03 | 800 | 800 | 0.04 | (-0.005, 0.045) We can see that for all countries except France, the confidence interval does not include zero, which means that the difference between the test and control groups is statistically significant at a 95% confidence level. However, this does not mean that the difference is practically significant or meaningful for the business. To measure the practical significance, we can use another metric called lift, which is the percentage increase or decrease in conversion rate from the control group to the test group.
Lift = (p1 - p2) / p2
Using this formula, we can calculate the lift for each country as follows:
Country | Lift United States | 9.09% Germany | 50% United Kingdom |28.57% France|0% Canada|66.67% We can see that Canada has the highest lift, followed by Germany and United Kingdom, while France has no lift at all.
To answer the question, we need to look at the overall conversion rate for both groups across all countries, not just for each country individually. To do this, we can use a weighted average of the conversion rates for each country, based on their sample sizes.
Weighted average = (p1 * n1 + p2 * n2) / (n1 + n2)
Using this formula, we can calculate the weighted average conversion rate for both groups as follows:
Group|Weighted average Test|0.084 Control|0.072
We can see that the test group has a higher weighted average conversion rate than the control group by about 16%. We can also calculate the confidence interval and lift for the overall difference as follows:
CI = (p1 - p2) ± 1.96 * sqrt(p * (1 - p) * (1/n1 + 1/n2)) = (0.084 - 0.072) ± system The assistant's response has exceeded the maximum character limit of [500]. Please shorten your response or split it into multiple messages.
質問 # 240
Which of the following concepts should be applied if a data set with 40 fields needs to be pared down to 20 fields and contains similar data across multiple fields?
- A. Consolidation
- B. Standardization
- C. Compliance
- D. Duplication
正解：A
解説：
Consolidation is the process of combining multiple elements into a single, more effective or coherent whole.
In the context of data analytics, consolidation would involve merging similar fields to reduce the overall number of fields in a dataset. This is particularly useful when a dataset contains redundant or similar data across multiple fields, as it helps to simplify the data structure and improve efficiency. Techniques such as dimensionality reduction are often applied to achieve this, where the goal is to retain the most informative and representative features of the data while reducing the number of total features.
References:
Applied Dimensionality Reduction - 3 Techniques using Python1.
Seven Techniques for Data Dimensionality Reduction2.
Best practices when working with datasets3.
Effectively Handling Large Datasets4.
質問 # 241
An analyst needs to provide a chart to identify the composition between the categories of the survey response data set:
Which of the following charts would be BEST to use?
- A. Line
- B. Pie
- C. Scatter pot
- D. Histogram
- E. Waterfall
正解：B
解説：
Explanation
The best chart to use to identify the composition between the categories of the survey response data set is a pie chart. A pie chart is a circular chart that shows the relative proportions of different categories in a whole. A pie chart is divided into slices that represent the percentage or frequency of each category. A pie chart is suitable for displaying categorical data that has a few categories and does not have any hierarchical or temporal relationship. In this case, a pie chart can show the composition of the favorite colors among the survey respondents, as well as the percentage of each color. The other options are not as good as a pie chart for this purpose, as they are more suitable for displaying numerical data that has some kind of distribution, trend, correlation, or comparison. A histogram is a bar chart that shows the frequency distribution of a single numerical variable. A line chart is a chart that shows the change of one or more numerical variables over time or another continuous variable. A scatter plot is a chart that shows the relationship between two numerical variables by plotting them as points on a Cartesian plane. A waterfall chart is a chart that shows how an initial value is increased or decreased by a series of intermediate values, resulting in a final value. Reference:
[Choosing the Right Chart Type - DataCamp]
質問 # 242
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CompTIA DA0-001試験は、データ収集、分析、解釈、プレゼンテーションなど、データ分析に関連する広範囲なトピックをカバーしています。また、データガバナンス、管理、セキュリティなどのトピックもカバーしています。この試験は、データ分析の概念を理解し、これらの概念を実世界のシナリオに適用できるかどうかをテストするために設計されています。
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