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合格させるCompTIA DA0-001試験問題でテスト復刻エンジンとPDF
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質問 # 78
Which of the following is an example of a discrete data type?
- A. 8in (20cm)
- B. 5 kids
- C. 2.5mi (4km)
- D. 10.7lbs (4.9kg)
正解：B
解説：
Explanation
A discrete data type is a data type that can only take on a finite number of values, such as integers or categories. An example of a discrete data type is the number of kids, as it can only be a whole number. The other options are examples of continuous data types, as they can take on any value within a range. The length in inches or centimeters, the distance in miles or kilometers, and the weight in pounds or kilograms are all continuous data types. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy
質問 # 79
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. The new layout has the lowest conversion rates in the United Kingdom.
- B. In Germany, the increase in conversion from the new layout was not significant.
- C. In general, users who visit the new website are more likely to make a purchase.
- D. In France, the increase in conversion from the new layout was not significant.
正解：C
質問 # 80
Which one of the following programming languages is specifically designed for use in analytics applications?
- A. Java.
- B. Python.
- C. R
- D. C++
正解：C
質問 # 81
An analyst has conducted a review of business questions. Which of the following should the analyst do next to conduct an analysis?
- A. Determine the data needs and review the observations.
- B. Determine the data needs and schedule interviews.
- C. Determine the data needs and sources for analysis.
- D. Determine the data needs and begin the analysis.
正解：C
解説：
Explanation
After conducting a review of the business questions, the next step for the analyst is to determine the data needs and sources for analysis. This involves identifying the relevant data elements, variables, and metrics that are required to answer the business questions, as well as the data sources, formats, and quality that are available to access and use. This step will help the analyst to plan the data collection, preparation, and integration processes, as well as to assess the feasibility and limitations of the analysis1.
質問 # 82
While reviewing survey data, a research analyst notices data is missing from all the responses to a single question. Which of the following methods would BEST address this issue?
- A. Replace redundant data.
- B. Replace missing data.
- C. Remove invalid data.
- D. Remove duplicate data.
正解：B
解説：
Explanation
This is because missing data is a type of data quality issue that occurs when data is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis or process. Missing data can be caused by various factors, such as human error, system error, or non-response. Missing data can be addressed by using various methods, such as replacing missing data, which means filling in or imputing the missing values with some reasonable estimates, such as mean, median, mode, or regression. The other methods are not used to address missing data. Here is why:
Remove duplicate data is a type of method that eliminates or reduces duplicate data, which is a type of data quality issue that occurs when data is repeated or copied in a data set. Removing duplicate data does not address missing data, but rather affects the quantity and validity of the data.
Replace redundant data is a type of method that eliminates or reduces redundant data, which is a type of data quality issue that occurs when data is unnecessary or irrelevant for the analysis or purpose.
Replacing redundant data does not address missing data, but rather affects the efficiency and performance of the analysis or process.
Remove invalid data is a type of method that eliminates or reduces invalid data, which is a type of data quality issue that occurs when data is incorrect or inaccurate in a data set. Removing invalid data does not address missing data, but rather affects the validity and reliability of the analysis or process.
質問 # 83
Given the following graph:
Which of the following summary statements upholds integrity in data reporting?
- A. While Strategy 2 does not result in the highest sales of Product D, over all products it appears to be the most effective.
- B. Strategy 4 provides the best sales in comparison to other strategies.
- C. Sales are approximately equal for Product A and Product B across all strategies.
- D. Product D should be promoted more than the other products in all strategies.
正解：B
質問 # 84
Data validation should occur only when data is initially brought into a organization.
- A. False.
- B. True.
正解：A
質問 # 85
Which of the following data elements would not normally be stored in binary format?
- A. Photograph.
- B. Video recording.
- C. Audio recording.
- D. Geolocation.
正解：D
質問 # 86
A data analyst has been asked to create a sales report that calculates the rolling 12-month average for sales. If the report will be published on November 1, 2020, which of the following months shouts the report cover?
- A. October 31, 2019 to October 31, 2020
- B. October 31, 2020 to November 1, 2021
- C. November 1, 2019 to October 31, 2020
- D. October 1, 2019 to October 31, 2020
正解：C
質問 # 87
Which of the following is used for calculations and pivot tables?
- A. Domo
- B. Microsoft Excel
- C. SAS
- D. IBM SPSS
正解：B
解説：
Explanation
This is because Microsoft Excel is a type of software application that allows users to create, edit, and analyze data in spreadsheets, which are composed of rows and columns of cells that can store various types of data, such as numbers, text, or formulas. Microsoft Excel can be used for calculations and pivot tables, which are two common features or functions in data analysis. Calculations are mathematical operations or expressions that can be performed on the data in the cells, such as addition, subtraction, multiplication, division, average, sum, etc. Pivot tables are interactive tables that can summarize and display the data in different ways, such as by grouping, filtering, sorting, or aggregating the data based on various criteria or categories. The other software applications are not used for calculations and pivot tables. Here is why:
IBM SPSS is a type of software application that allows users to perform statistical analysis and modeling on data sets, such as regression, correlation, ANOVA, etc. IBM SPSS does not use spreadsheets or cells to store or manipulate data, but rather uses data views or variable views to display the data in rows and columns. IBM SPSS does not have pivot tables as a feature or function, but rather has output views or charts to display the results of the analysis.
SAS is a type of software application that allows users to perform data management and analysis using a programming language that consists of statements and commands. SAS does not use spreadsheets or cells to store or manipulate data, but rather uses data sets or tables that are stored in libraries or folders. SAS does not have pivot tables as a feature or function, but rather has procedures or macros that can produce summary tables or reports based on the data.
Domo is a type of software application that allows users to create and share dashboards and visualizations that display data from various sources and systems, such as databases, cloud services, or web applications. Domo does not use spreadsheets or cells to store or manipulate data, but rather uses connectors or APIs to access and integrate the data from different sources. Domo does not have pivot tables as a feature or function, but rather has cards or widgets that can show different aspects or metrics of the data.
質問 # 88
When analyzing the values of two variables, you decide to convert both variables so they are on a scale of 0 to 1.
What term describes this action?
- A. Normalization.
- B. Transposition.
- C. Aggregation.
- D. Filtering.
正解：A
解説：
Normalization is the process of reorganizing data in a database so that it meets two basic requirements: There is no redundancy of data, all data is stored in only one place. Data dependencies are logical, all related data items are stored together.
Put simply, data normalization ensures that your data looks, reads, and can be utilized the same way across all of the records in your customer database. This is done by standardizing the formats of specific fields and records within your customer database.
質問 # 89
Given the diagram below:
Which of the following data schemas shown?
- A. Online transactional processing
- B. Relational database
- C. Data lake
- D. Key-value pairs
正解：B
質問 # 90
An analyst has received the requirements for an internal user dashboard. The analyst confirms the data sources and then creates a wireframe. Which of the following is the NEXT step the analyst should take in the dashboard creation process?
- A. Deploy to production.
- B. Create subscriptions.
- C. Optimize the dashboard.
- D. Get stakeholder approval.
正解：D
解説：
Explanation
Getting stakeholder approval is the next step the analyst should take in the dashboard creation process, after confirming the data sources and creating a wireframe. Stakeholder approval means getting feedback and validation from the intended users or clients of the dashboard, to ensure that it meets their expectations and requirements. This step helps to avoid rework and ensure customer satisfaction. References: CompTIA Data+ Certification Exam Objectives, page 14
質問 # 91
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. The new layout has the lowest conversion rates in the United Kingdom.
- B. In Germany, the increase in conversion from the new layout was not significant.
- C. In general, users who visit the new website are more likely to make a purchase.
- D. In France, the increase in conversion from the new layout was not significant.
正解：C
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 # 92
A data analyst has been asked to derive a new variable labeled "Promotion_flag" based on the total quantity sold by each salesperson. Given the table below:
Which of the following functions would the analyst consider appropriate to flag "Yes" for every salesperson who has a number above 1,000,000 in the Quantity_sold column?
- A. Mathematical
- B. Date
- C. Logical
- D. Aggregate
正解：C
質問 # 93
Given the diagram below:
Which of the following types of sampling is depicted in the image?
- A. Stratified
- B. Random
- C. Systematic
- D. Cluster
正解：C
解説：
Explanation
Systematic sampling is a type of sampling where the sample is selected by following a fixed interval. For example, every 10th person in a list is chosen for the sample. In the image, the sample is selected by choosing every 3rd person in the line, starting from person number 1. This is an example of systematic sampling.
References: Types of Sampling Techniques in Data Analytics You Should Know, Sampling Methods | Types, Techniques & Examples - Scribbr
質問 # 94
Samantha needs to share a list of her organization's top 50 customers with the VP of sales.
She would like to include the name of the customer, the business they represent, their contact information, and their total sales over the past year.
The VP does not have any specialized analytics skills or software but would like to make some personal notes on the dataset.
What would be the best tool for Samantha to use to share this information?
- A. Minitab.
- B. Power BI.
- C. Microsoft Excel.
- D. SAS.
正解：C
解説：
Microsoft Excel.
This scenario presents a very simple use case where the business leader needs a dataset in an easy-to-access form and will not be performing any detailed analysis.
A simple spreadsheet, such as Microsoft Excel, would be the best tool for this job.
There is no need to use a statistical analysis package, such as SAS or Minitab, as this would likely confuse the VP without adding any value. The same is true of an integrated analytics suite, such as Power BI.
質問 # 95
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(address, ' , ' , regionname) AS full_address FROM melb LIMIT 5;
- B. SELECT CONCAT(regionname, '-' , address) AS full_address FROM melb LIMIT 5;
- C. SELECT CONCAT(address, '-' , regionname) AS full_address FROM melb LIMIT 5;
- D. SELECT CONCAT(regionname, ' , ' , address) AS full_address FROM melb LIMIT 5
正解：A
解説：
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.
質問 # 96
Which of the following are reasons to create and maintain a data dictionary? (Choose two.)
- A. To specify user groups for databases
- B. To reduce processing power requirements
- C. To confine breaches of PHI data
- D. To provide continuity through personnel turnover
- E. To improve data acquisition
- F. To remember specifics about data fields
正解：D、F
解説：
Explanation
A data dictionary is a collection of metadata that describes the data elements in a database or dataset. It can help improve data acquisition by providing information about the data sources, formats, quality, and usage. It can also help remember specifics about data fields, such as their names, definitions, types, sizes, and relationships. Therefore, options B and D are correct.
Option A is incorrect because it is not a reason to create and maintain a data dictionary, but a benefit of doing so.
Option C is incorrect because specifying user groups for databases is not a function of a data dictionary, but a function of a database management system or a security policy.
Option E is incorrect because confining breaches of PHI data is not a function of a data dictionary, but a function of a data protection or encryption system.
Option F is incorrect because reducing processing power requirements is not a function of a data dictionary, but a function of a data compression or optimization system.
質問 # 97
Given the table below:
Which of the following boxes indicates that a Type Il error has occurred?
- A. 0
- B. 1
- C. 2
- D. 3
正解：D
解説：
Explanation
A Type II error is a false negative conclusion, which means failing to reject a null hypothesis that is actually false. In the table, box 3 indicates that a Type II error has occurred, because it shows that the null hypothesis is accepted when it is false in reality. This means that the statistical test failed to detect a significant difference or relationship that actually exists. References: Type I & Type II Errors | Differences, Examples, Visualizations - Scribbr, Type I and type II errors - Wikipedia
質問 # 98
Maria is developing a script that will perform some common analytics tasks.
In order to improve the efficiency of her workflow, she is using a package called the Tidyverse.
What programming language is she using?
- A. Ruby
- B. R
- C. Python
- D. C++
正解：B
解説：
The tidyverse is a collection of packages for the R programming language designed to facilitate the analytics workflow.
The tidyverse is not available for Python, Ruby, or C++, all of which are general-purpose programming languages.
質問 # 99
Which of the following is an example of structured data?
- A. An email
- B. A credit card number
- C. A photo
- D. Social media correspondence
正解：B
解説：
Explanation
A credit card number is an example of structured data, which is a type of data that conforms to a data model, has a well-defined structure, follows a consistent order, and can be easily accessed and used by a person or a computer program. A credit card number consists of 16 digits that are divided into four groups of four digits each, separated by spaces or hyphens. The first six digits indicate the issuer identification number, the next nine digits indicate the account number, and the last digit is a check digit that validates the number. A credit card number can be stored and processed in a structured format, such as a database or a spreadsheet1.
質問 # 100
A user receives a large custom report to track company sales across various date ranges. The user then completes a series of manual calculations for each date range. Which of the following should an analyst suggest so the user has a dynamic, seamless experience?
- A. Add macros to the report to speed up the filtering and calculations process.
- B. Create multiple reports, one for each needed date range.
- C. Create a dashboard with a date range picker and calculations built in.
- D. Build calculations into the report so they are done automatically.
正解：C
解説：
Explanation
Create a dashboard with a date range picker and calculations built in. This is because a dashboard is a type of visualization that displays multiple charts or graphs on a single page, usually to provide an overview or summary of some data or information. A dashboard can be used to track company sales across various date ranges by showing different metrics and indicators related to sales, such as revenue, volume, or growth. By creating a dashboard with a date range picker and calculations built in, the analyst can suggest a way for the user to have a dynamic, seamless experience, which means that the user can interact with and customize the dashboard according to their needs or preferences, as well as avoid any manual work or errors. For example, a date range picker is a type of feature or function that allows users to select or adjust the time period for which they want to see the data on the dashboard, such as daily, weekly, monthly, or quarterly. A date range picker can make the dashboard dynamic, as it can automatically update or refresh the dashboard with new data based on the selected time period. Calculations are mathematical operations or expressions that can be performed on the data on the dashboard, such as addition, subtraction, multiplication, division, average, sum, etc.
Calculations can make the dashboard seamless, as they can eliminate the need for manual calculations for each date range, as well as ensure accuracy and consistency of the results. The other ways are not the best ways to provide a dynamic, seamless experience for the user. Here is why:
Creating multiple reports, one for each needed date range would not provide a dynamic, seamless experience for the user, but rather create a static, cumbersome experience, which means that the user cannot interact with or customize the reports according to their needs or preferences, as well as have to deal with multiple files or pages. For example, creating multiple reports would make it difficult for the user to compare or contrast the sales across different date ranges, as well as increase the workload and complexity of managing and maintaining the reports.
Building calculations into the report so they are done automatically would not provide a dynamic, seamless experience for the user, but rather provide a partial, limited experience, which means that the user can only benefit from one aspect or feature of the report, but not from others. For example, building calculations into the report would help with avoiding manual work or errors, but it would not help with interacting with or customizing the report according to different date ranges.
Adding macros to the report to speed up the filtering and calculations process would not provide a dynamic, seamless experience for the user, but rather provide an advanced, complex experience, which means that the user would need to have some technical skills or knowledge to use or apply the macros, as well as face some potential risks or challenges. For example, adding macros to the report would require the user to know how to write or run the macros, which are a type of code or script that automates certain tasks or actions on the report, such as filtering or calculating the data. Adding macros to the report could also expose the user to some security or compatibility issues, such as viruses, malware, or errors.
質問 # 101
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2023年最新の実際に出ると確認されたCompTIA DA0-001無料試験問題：https://drive.google.com/open?id=1UZz2pmUds-q-Z2p3pMoCW6PlFpOWn5rL