2025年最新のDA0-001試験資料DA0-001学習ガイド [Q99-Q121]

2025年最新のDA0-001試験資料DA0-001学習ガイド

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質問 # 99
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 with views for team, individuals, and management. Configure permissions to control access.
• B. Create a dashboard displaying a data refresh date so users know the current sales numbers and configure permissions to control access.
• C. Create a dashboard for sales numbers, pipeline, and team and individual performance for the management team.
• D. Create a dashboard with filters for the overall team, individuals, and management. Users can filter to see the data they want.

正解：C

質問 # 100
Which of the following data sampling methods involves dividing a population into subgroups by similar characteristics?

• A. Simple random
• B. Convenience
• C. Stratified
• D. Systematic

正解：C

解説：
Explanation
Stratified sampling is a data sampling method that involves dividing a population into subgroups by similar characteristics, such as age, gender, income, etc. Then, a simple random sample is drawn from each subgroup.
This method ensures that each subgroup is adequately represented in the sample and reduces the sampling error. References: CompTIA Data+ Certification Exam Objectives, page 11.

質問 # 101
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. Create multiple reports, one for each needed date range.
• B. Build calculations into the report so they are done automatically.
• C. Create a dashboard with a date range picker and calculations built in.
• D. Add macros to the report to speed up the filtering and calculations process.

正解：C

質問 # 102
Which of the following activities occurs during the ETL process?

• A. Multiplying unique data
• B. Reviewing and addressing missing values
• C. Inserting a pivot table and pivot chart
• D. Creating a dashboard

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation:
ETL stands for Extract, Transform, Load, which are the three fundamental steps in the data integration process:
* Extract:Retrieving data from various source systems.
* Transform:Cleaning and converting the extracted data into a suitable format or structure for analysis.
* Load:Inserting the transformed data into a target database or data warehouse.
Option A:Reviewing and addressing missing values
* Rationale:During theTransformphase of the ETL process, data is cleansed and prepared for analysis.
This includes reviewing and addressing missing values to ensure data quality and consistency. Handling missing data is crucial, as it can impact the accuracy of analyses and decision-making.

質問 # 103
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 general, users who visit the new website are more likely to make a purchase.
• C. In France, the increase in conversion from the new layout was not significant.
• D. In Germany, the increase in conversion from the new layout was not significant.

正解：B

質問 # 104
A data analyst needs to create a dashboard to help identify trends in the data sets. Which of the following is an appropriate consideration for dashboard development?

• A. A comparison of data sets
• B. A report from the data source
• C. Data sources and attributes
• D. Frequently asked questions

正解：C

解説：
When creating a dashboard to identify trends in data sets, the most appropriate consideration is the data sources and attributes. This is because the quality, reliability, and structure of the data sources directly influence the dashboard's ability to accurately reflect trends. Attributes, such as the type of data and the time frame it covers, are crucial for trend analysis. A well-designed dashboard should provide a clear and intuitive representation of the data, allowing for easy identification of trends and patterns.
Frequently asked questions (B) can inform the design of the dashboard but are not a direct consideration for the development process itself. A report from the data source might be an output of the dashboard but does not guide its development. A comparison of data sets (D) could be a feature of the dashboard, but the underlying data sources and attributes must be considered first to ensure accurate and meaningful comparisons.
References:
* Best practices in dashboard design emphasize the importance of understanding and consolidating different data sources and creating a mix of useful metrics, which aligns with the choice of data sources and attributes1.
* Fundamental dashboard design principles include the clear and efficient display of information, which is dependent on the proper selection and use of data sources and attributes2.
* Effective dashboard communication is achieved by using colors, shapes, sizes, labels, and legends meaningfully, all of which rely on the underlying data sources and attributes3.

質問 # 105
A data analyst needs to observe the relationship between two numeric variables and identify the clustering pattern as well as the outliers. Which of the following visualizations should the analyst use?

• A. Stacked chart
• B. Tree map
• C. Heat map
• D. Scatter plot

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation:
When analyzing relationships betweentwo numeric variables, the best visualization is ascatter plot, as it helps to:
* Identifycorrelations(positive, negative, or no correlation).
* Detectclustering patternsin data points.
* Spotoutliersthat deviate significantly from the general trend.
* Option A (Heat map):Incorrect. Heat maps are used for visualizing intensity differences across categories, not for identifying patterns between two numeric variables.
* Option B (Tree map):Incorrect. Tree maps visualize hierarchical relationships, not scatter patterns.
* Option C (Scatter plot):Correct.Scatter plots are best suited for examining relationships and identifying clustering patterns and outliers.
* Option D (Stacked chart):Incorrect. Stacked charts are used to display the composition of multiple data series, not relationships between two numeric variables.

質問 # 106
Which of the following best describes how discrete data differs from continuous data?

• A. Discrete data cannot create a sloped line.
• B. Discrete data can only be a finite number of values.
• C. Discrete data can have decimal points.
• D. Discrete data applies only to numbers.

正解：B

解説：
Discrete data are data that can only assume specific values that are countable and distinct. For example, the number of books, the number of heads in a coin toss, or the number of patients in a hospital are discrete data. Discrete data cannot have fractional or decimal values, and there are clear spaces between the possible values12.
Continuous data are data that can assume any value within a range and can be meaningfully divided into smaller parts. For example, the weight, height, length, time, or temperature are continuous data. Continuous data can have fractional or decimal values, and there are infinite numbers of possible values between any two points12.

質問 # 107
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. Waterfall
• B. Histogram
• C. Scatter pot
• D. Pie
• E. Line

正解：D

解説：
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 # 108
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：B

解説：
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. Reference: Type I & Type II Errors | Differences, Examples, Visualizations - Scribbr, Type I and type II errors - Wikipedia

質問 # 109
A data analyst is attempting to understand how ice cream consumption is affected by different attributes. such as cost, temperature. and income level. Which of the following regression analyses should the data analyst perform to understand this relationship?

• A. Logistic
• B. Cox
• C. Polynomial
• D. Ordinary least squares

正解：D

解説：
Ordinary least squares
Ordinary least squares (OLS) is a type of linear regression that is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Use when: The relationship between the predictor variable(s) and the response variable is reasonably linear.The response variable is a continuous numeric variable1.
In this case, the data analyst is interested in understanding how ice cream consumption (the response variable) is affected by different attributes, such as cost, temperature, and income level (the predictor variables).
Assuming that these variables have a linear relationship, OLS can be used to estimate the coefficients of the regression equation that best fits the data.OLS can also provide measures of goodness-of-fit, such as R- squared and adjusted R-squared, and test the significance of the coefficients using t-tests and F-tests2.
Option A is incorrect, as logistic regression is used to fit a regression model that describes the relationship between one or more predictor variables and a binary response variable.Use when: The response variable is binary - it can only take on two values1. Ice cream consumption is not a binary variable, but rather a continuous numeric variable.
Option C is incorrect, as Cox regression is used to fit a regression model that describes the relationship between one or more predictor variables and a survival time response variable.Use when: The response variable is the time until an event of interest occurs, such as death, failure, or recovery3. Ice cream consumption is not a survival time variable, but rather a continuous numeric variable.
Option D is incorrect, as polynomial regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable.Use when: The relationship between the predictor variable(s) and the response variable is non-linear1. If there is no evidence of non-linearity in the data, polynomial regression may not be appropriate, as it may overfit the data and produce unreliable estimates.

質問 # 110
Which of the following actions should be taken when transmitting data to mitigate the chance of a data leak occurring? (Choose two.)

• A. Data identification
• B. Data processing
• C. Data masking
• D. Data Reporting
• E. Data encryption
• F. Fata removal

正解：C、E

解説：
Data encryption and data masking are two actions that can be taken when transmitting data to mitigate the chance of a data leak occurring. Data encryption means transforming data into an unreadable format that can only be decrypted with a key. Data masking means hiding or replacing sensitive data with fictitious or anonymized data. Both methods protect the confidentiality and integrity of the data in transit. References:
CompTIA Data+ Certification Exam Objectives, page 13

質問 # 111
Which of the following descriptive statistical methods are measures of central tendency? (Choose two.)

• A. Correlation
• B. Mean
• C. Variance
• D. Mode
• E. Minimum
• F. Maximum

正解：B、D

質問 # 112
Given the image below:

Which of the following file formats is depicted?

• A. CSV
• B. HTML
• C. XML
• D. JSON

正解：D

解説：
The image depicts a snippet of code in the JSON format, which stands for JavaScript Object Notation. JSON is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language and is commonly used to transmit data in web applications.
* CSV, or Comma-Separated Values, is a simple file format used to store tabular data, such as a spreadsheet or database. It uses commas to separate values.
* XML, or eXtensible Markup Language, is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable.
* HTML, or HyperText Markup Language, is the standard markup language for documents designed to be displayed in a web browser.
References:
* JSON.org - Introducing JSON1
* W3Schools - JSON Introduction2
* Mozilla Developer Network - JSON3

質問 # 113
Which of following is a non-relational database?

• A. MySQL
• B. Neo4j
• C. PostgreSQL
• D. SQLite

正解：B

解説：
Explanation
Neo4j is a type of non-relational database that uses a graph model to store data. A graph database is a database that represents data as nodes and edges, where nodes are entities and edges are relationships between them. A graph database can store complex and diverse data that is not easily structured in tables. A graph database can also perform fast and efficient queries on the data by traversing the connections between the nodes

質問 # 114
What feature varies on a bubble chart but not on a scatter plot?

• A. Size
• B. Y-position
• C. X-position
• D. Color

正解：A

質問 # 115
Which of the following technologies would be best suited for creating a multiple linear regression model?

• A. SQL
• B. Microsoft Power Bl
• C. Tableau
• D. R

正解：D

解説：
R is a statistical programming language that is specifically designed for data analysis and statistical modeling, making it highly suitable for creating a multiple linear regression model. It has extensive libraries such as lm() for linear modeling, which simplifies the process of model creation, diagnostics, and interpretation. R also provides robust tools for data manipulation and visualization, which are essential for preparing data for regression analysis and understanding the results123.
While Microsoft Power BI, SQL, and Tableau have capabilities for regression analysis, they are more limited compared to R. Power BI and Tableau are primarily business intelligence tools that offer some built-in analytics capabilities, but they are not as comprehensive as R. SQL is a database query language that can perform some statistical calculations, but it is not inherently designed for statistical modeling4567.
Reference:
Multiple Linear Regression in R: Tutorial With Examples - DataCamp1.
Implementing linear regression in Power BI - SQLBI5.
Choosing a Predictive Model - Tableau6.
How Predictive Modeling Functions Work in Tableau7.

質問 # 116
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.

正解：D

解説：
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.

質問 # 117
A data analyst for a media company needs to determine the most popular movie genre. Given the table below:

Which of the following must be done to the Genre column before this task can be completed?

• A. Delimit
• B. Merge
• C. Concatenate
• D. Append

正解：A

質問 # 118
'Which of the following is the BEST reason to use database views instead of tables?

• A. Views can be used to restrict sensitive information.
• B. Views allow for the joining of multiple data sources, whereas tables do not.
• C. Views reduce the need for repetitive, complex data joins.
• D. Views allow for the storage of temporary data. whereas tables do not.

正解：C

解説：
Explanation
Views are virtual tables that are created by querying one or more base tables or other views. Views do not store any data, but only show the result of a query. One of the main advantages of using views is that they can reduce the need for repetitive, complex data joins. For example, if a query involves joining multiple tables with many conditions, creating a view can simplify the query and make it easier to reuse. Therefore, the correct answer is A. References: [What is a Database View? | Definition & Examples - Vertabelo], [Database Views - GeeksforGeeks]

質問 # 119
Which of the following is the best reason for removing data outliers?

• A. Data varies significantly from others.
• B. Data is duplicated in the whole range.
• C. Data is redundant in the table.
• D. Data is missing from the table.

正解：A

解説：
Comprehensive and Detailed In-Depth
Data outliers are observations that deviate markedly from other observations in the dataset. Handling outliers appropriately is crucial in data analysis to ensure the accuracy and reliability of insights derived from the data.
Option A:Data varies significantly from others.
Rationale:Outliers are data points that differ significantly from other observations. They can skew statistical analyses, leading to misleading results. Removing or addressing outliers can help in achieving a more accurate representation of the data, ensuring that analyses and models are not unduly influenced by anomalous values.
Reference:
partners.comptia.org
Option B:Data is redundant in the table.
Rationale:Redundant data refers to unnecessary repetition of data within the dataset. While removing redundancy is a part of data cleansing, it pertains to duplicate entries rather than outliers.
Option C:Data is duplicated in the whole range.
Rationale:Duplicate data points are exact copies of existing entries. Removing duplicates is essential for data accuracy but is a separate issue from handling outliers.
Option D:Data is missing from the table.
Rationale:Missing data refers to the absence of values in the dataset. Addressing missing data is crucial, but it involves different techniques such as imputation, rather than the removal processes associated with outliers.
Conclusion:The primary reason for removing data outliers is that they vary significantly from other data points, which can distort statistical analyses and lead to incorrect conclusions. Properly managing outliers ensures the robustness and reliability of data-driven decisions.
CompTIA Data+ Certification Exam Objectives:
partners.comptia.org
CompTIA Data+ Study Guide: Exam DA0-001:

質問 # 120
After the daily ETL jobs are completed, the data in the reports does not appear complete, and a lot of data seems to be missing. Which of the following concepts should be used to assess and investigate further?

• A. Cross-validation
• B. Data consistency
• C. Data integrity
• D. Data profiling

正解：D

解説：
Comprehensive and Detailed In-Depth Explanation:
When encountering issues where reports are incomplete or data appears to be missing after ETL (Extract, Transform, Load) processes, it's essential to assess the quality and structure of the data.Data profilingis the process of examining the data available in an existing data source and collecting statistics and information about that data. This practice helps in understanding the data's condition, identifying anomalies, and ensuring that the data conforms to the expected patterns.
Option A:Cross-validation
* Rationale:Cross-validation is a statistical method used to estimate the skill of machine learning models.
It is primarily used in predictive modeling to assess how the results of a statistical analysis will generalize to an independent dataset. While valuable in model evaluation, it doesn't address issues related to missing or incomplete data in ETL processes.
Option B:Data profiling
* Rationale:Data profiling involves analyzing the data for accuracy and completeness. By performing data profiling, analysts can identify missing values, inconsistencies, and anomalies within the dataset.
This process is crucial for diagnosing issues that arise during ETL operations, such as incomplete data loads or transformation errors.

質問 # 121
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