DA0-002ブレーン問題集PDF、CompTIA DA0-002試験問題豪華お試しセット
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質問 # 15
A data analyst needs to join together a table data source and a web API data source using Python. Which of the following is the best way to accomplish this task?
- A. Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together.
- B. Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together.
- C. Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together.
- D. Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together.
正解：A
解説：
This question falls under theData Acquisition and Preparationdomain of CompTIA Data+ DA0-002, which involves acquiring and combining data from different sources, such as a database and a web API, using tools like Python. The task requires joining the data, which in Python often involves using pandas DataFrames.
* Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together (Option A): VARCHAR is a databasedata type for strings, not a format for data exchange or merging in Python, making this incorrect.
* Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together (Option B): Web APIs commonly return data in JSON format, and databases can export data as JSON. In Python, JSON data can be easily converted to pandas DataFrames using pandas.read_json() or pandas.DataFrame(), and then merged using pandas.merge() on a common key, making this the best approach.
* Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together (Option C): TXT is a generic text format that lacks structure, making it less efficient for merging compared to JSON.
* Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together (Option D): Converting to a string format is vague and not a standard approach for structured data merging in Python.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as combining data from APIs and databases, and JSON is a standard format for this purpose in Python.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
質問 # 16
A data analyst needs to provide a weekly sales report for the Chief Financial Officer. Which of the following delivery methods is the most appropriate?
- A. A granular daily report in a dashboard
- B. A high-level email
- C. A spreadsheet with raw data
- D. A detailed text document
正解：B
解説：
This question pertains to theVisualization and Reportingdomain, focusing on report delivery methods for a specific audience. The Chief Financial Officer (CFO) needs a weekly sales report,suggesting a concise, executive-level summary.
* A granular daily report in a dashboard (Option A): Daily granularity is too frequent for a weekly report, and a dashboard might be too interactive for a CFO's needs.
* A detailed text document (Option B): A detailed document is too lengthy for a CFO, who typically needs a summary.
* A spreadsheet with raw data (Option C): Raw data requires further analysis, which isn't appropriate for an executive-level report.
* A high-level email (Option D): A high-level email provides a concise summary of weekly sales, tailored for an executive like a CFO, making it the most appropriate delivery method.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a high-level email is best for delivering a weekly summary to a CFO.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
質問 # 17
A product goes viral on social media, creating high demand. Distribution channels are facing supply chain issues because the testing and training models that are used for sales forecasting have not encountered similar demand. Which of the following best describes this situation?
- A. Incorrect sizing
- B. Skewing
- C. Data drift
- D. Model bias
正解：C
解説：
This question pertains to theData Analysisdomain, focusing on issues with forecasting models. The scenario describes a sudden change in demand (viral product) that the model couldn't predict because it hasn't seen similar patterns before.
* Model bias (Option A): Model bias occurs when a model systematically favors certain outcomes due to flawed training data, but this scenario is about a change in data patterns, not bias.
* Data drift (Option B): Data drift occurs when the statistical properties of the data change over time (e.
g., sudden high demand due to virality), causing the model to perform poorly because it was trained on different patterns, which fits the scenario.
* Incorrect sizing (Option C): This term is vague and not a standard concept in data analysis for this context.
* Skewing (Option D): Skewing refers to data distribution asymmetry, not a change in data patterns affecting model performance.
The DA0-002 Data Analysis domain includes understanding "applying the appropriate descriptive statistical methods," and data drift is a key concept in forecasting when data patterns change unexpectedly.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
質問 # 18
Software end users are happy with the quality of product support provided. However, they frequently raise concerns about the long wait time for resolutions. An IT manager wants to improve the current support process. Which of the following should the manager use for this review?
- A. Survey
- B. KPI
- C. Infographic
- D. UAT
正解：A
解説：
This question falls under theData Analysisdomain, focusing on methods to gather data for process improvement. The IT manager needs to review user concerns about wait times, which requires collecting feedback.
* Infographic (Option A): An infographic visualizes data but isn't a method for gathering feedback.
* KPI (Option B): KPIs (e.g., average resolution time) measure performance but don't directly gather user feedback.
* Survey (Option C): A survey collects detailed feedback from users about their experiences, such as wait times, making it the best method for this review.
* UAT (Option D): User Acceptance Testing validates software functionality, not support processes.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and surveys are a standard method for collecting user feedback to analyze and improve processes.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
質問 # 19
A Chief Executive Officer requests a report that must:
* Summarize the company metrics in a simple way.
* Be clear and concise.
* Be easily understood by all company levels.
* Be accessible and updated without manual intervention.
Which of the following communication approaches best meets these requirements?
- A. Key performance indicator dashboard
- B. Slide presentation
- C. Executive summary
- D. Open data portal
正解：A
解説：
This question pertains to theVisualization and Reportingdomain, focusing on selecting the appropriate communication method for a report. The requirements emphasize simplicity, clarity, accessibility, and automatic updates, which point to a specific approach.
* Executive summary (Option A): An executive summary is a written document that summarizes metrics but isn't typically updated automatically and may not be accessible toall levels without distribution.
* Slide presentation (Option B): A slide presentation can be clear but requires manual updates and isn't inherently accessible to all company levels.
* Key performance indicator dashboard (Option C): A KPI dashboard provides a simple, visual summary of metrics, is clear and concise, can be understood by all levels, and can be set up to update automatically, meeting all requirements.
* Open data portal (Option D): An open data portal provides raw data access, which may not be simple or easily understood by all levels.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a KPI dashboard is the best approach for meeting these requirements.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
質問 # 20
A data analyst receives four files that need to be unified into a single spreadsheet for further analysis. All of the files have the same structure, number of columns, and field names, but each file contains different values.
Which of the following methods will help the analyst convert the files into a single spreadsheet?
- A. Appending
- B. Clustering
- C. Parsing
- D. Merging
正解：A
解説：
This question is part of theData Acquisition and Preparationdomain, which involves combining data from multiple sources. The files have the same structure but different values, meaning theyneed to be stacked vertically into one dataset.
* Merging (Option A): Merging typically involves joining datasets on a common key (e.g., a customer ID), which isn't indicated here since the files only differ in values, not keys.
* Appending (Option B): Appending stacks datasets vertically, combining rows from files with the same structure into a single dataset, which matches the scenario.
* Parsing (Option C): Parsing involves breaking down data (e.g., splitting text), not combining files.
* Clustering (Option D): Clustering is a machine learning technique for grouping similar data points, not for combining files.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as appending datasets with identical structures.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
質問 # 21
Which of the following data repositories should a company use when structured data about the whole company needs to be stored in a predefined data structure?
- A. Data lake
- B. Data silo
- C. Data mart
- D. Data warehouse
正解：D
解説：
This question pertains to theData Concepts and Environmentsdomain, focusing on selecting the appropriate repository for structured data across an entire company. The requirement for a predefined structure narrows the options.
* Data mart (Option A): A data mart stores structured data for a specific business area (e.g., sales), not the whole company.
* Data warehouse (Option B): A data warehouse is designed to store structured data from across the entire company in a predefined schema, optimized for analytics and reporting.
* Data silo (Option C): A data silo is an isolated repository, often structured, but not designed for company-wide integration.
* Data lake (Option D): A data lake stores raw data (structured and unstructured) without a predefined structure, not suitable for this requirement.
The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data warehouse is ideal for company-wide structured data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
質問 # 22
Given the following dataset:
Day
Number of Guests
Monday
455
Tuesday
346
Wednesday
382
Thursday
563
Friday
887
Saturday
934
Sunday
346
Which of the following is the mode?
- A. 0
- B. 1
- C. 2
- D. 3
正解：A
解説：
This question falls under theData Analysisdomain, focusing on statistical measures. The mode is the value that appears most frequently in a dataset.
* Monday: 455
* Tuesday: 346
* Wednesday: 382
* Thursday: 563
* Friday: 887
* Saturday: 934
* Sunday: 346
The value 346 appears twice (Tuesday and Sunday), while all other values (455, 382, 563, 887, 934) appear once. Thus, the mode is 346.
* Option A: 346- Correct, as it's the most frequent value.
* Option B: 446- Incorrect, as 446 doesn't appear in the dataset.
* Option C: 455- Incorrect, as 455 appears only once.
* Option D: 559- Incorrect, as 559 doesn't appear in the dataset.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the mode is a fundamental measure of central tendency.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
質問 # 23
A sales manager wants to understand how sales are trending year over year. Which of the following chart types is the most appropriate to display the information?
- A. Donut
- B. Line
- C. Hierarchy
- D. Bubble
正解：B
解説：
This question falls under theVisualization and Reportingdomain, focusing on selecting the appropriate visualization for a specific data trend. The task is to show sales trends over time (year over year).
* Line (Option A): Line charts are ideal for displaying trends over time, such as year-over-year sales, as they clearly show changes and patterns across a continuous time axis.
* Donut (Option B): Donut charts show proportions or percentages of a whole, not suitable for time- based trends.
* Bubble (Option C): Bubble charts display three dimensions of data (e.g., size, x-axis, y-axis), not ideal for simple time trends.
* Hierarchy (Option D): Hierarchy charts (e.g., treemaps) show nested relationships, not time-based trends.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a line chart is best for time-series trends.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
質問 # 24
Which of the following allows a data analyst to send out a spreadsheet containing sensitive information without revealing personal details?
- A. Using a UUID in the data file
- B. Redacting all PII
- C. Adding access controls to the ID column
- D. Encrypting the spreadsheet
正解：B
解説：
This question pertains to theData Governancedomain, focusing on data privacy and security. The task is to share a spreadsheet with sensitive information while protecting personal details.
* Using a UUID in the data file (Option A): A UUID (Universally Unique Identifier) can anonymize records, but if other PII (e.g., names) remains, personal details are still exposed.
* Redacting all PII (Option B): Redacting personally identifiable information (PII) removes sensitive details (e.g., names, addresses), ensuring personal information isn't revealed while sharing the spreadsheet.
* Adding access controls to the ID column (Option C): Access controls limit who can view the data, but the question focuses on the spreadsheet content itself, not access.
* Encrypting the spreadsheet (Option D): Encryption protects the file during transmission, but once opened, personal details are still visible unless redacted.
The DA0-002 Data Governance domain includes "data privacy concepts," and redacting PII is the most direct method to protect personal details in a shared spreadsheet.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
質問 # 25
A sales manager wants a dashboard that shows sales aggregated by region and identifies high-volume sales by salesperson per region. Which of the following communication techniques best displays this information?
- A. Filter options
- B. Defined parameters
- C. Level of detail
- D. User persona
正解：A
解説：
This question pertains to theVisualization and Reportingdomain, focusing on dashboard features for displaying data. The dashboard needs to show aggregated sales by region and allow identification of high- volume sales by salesperson within each region.
* Defined parameters (Option A): Parameters set specific values (e.g., a date range), but they don't directly enable interaction with aggregated data.
* Filter options (Option B): Filter options allow the user to select a region and then view salespersons within that region, enabling the identification of high-volume sales per region interactively.
* Level of detail (Option C): Level of detail determines the granularity of data shown but doesn't facilitate interactive exploration.
* User persona (Option D): User personas guide dashboard design but aren't a communication technique for displaying data.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and filter options best enable the interactive analysis required.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
質問 # 26
A data analyst is following up on a recent, company-wide data audit of customer invoice data. Which of the following is the best option for the analyst to use?
- A. ISO
- B. GDPR
- C. PCI DSS
- D. PII
正解：B
解説：
This question falls under theData Governancedomain of CompTIA Data+ DA0-002, which includes understanding compliance frameworks for data audits, especially for customer data. The scenario involves a data audit of customer invoice data, which likely contains personal information, making privacy regulations relevant.
* PCI DSS (Option A): PCI DSS (Payment Card Industry Data Security Standard) applies specifically to payment card data, not general customer invoice data unless credit card details are involved, which isn't specified.
* GDPR (Option B): GDPR (General Data Protection Regulation) is a comprehensive privacy regulation for handling personal data of EU citizens, including customer invoice data, which may contain PII like names and addresses. It's the most relevant for a company-wide data audit.
* ISO (Option C): ISO standards (e.g., ISO 27001) relate to information security management but are not specific to customer data privacy audits.
* PII (Option D): PII (Personally Identifiable Information) is a concept, not a framework or tool for conducting an audit.
The DA0-002 Data Governance domain emphasizes "identifying PII and data privacy concepts," and GDPR is the most appropriate framework for auditing customer data to ensure compliance with privacy laws.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
質問 # 27
Which of the following best describes the semi-structured data that is gathered when web scraping?
- A. CSS
- B. JSON
- C. HTML
- D. CSV
正解：B
解説：
This question pertains to theData Acquisition and Preparationdomain, which in DA0-002 includes understanding data acquisition concepts and the types of data gathered from varioussources, such as web scraping. Web scraping involves extracting data from websites, and the data gathered is often semi-structured, meaning it has some organizational structure but isn't fully relational like a database table.
* JSON (Option A): JSON (JavaScript Object Notation) is a semi-structured data format commonly used in web applications. Web scraping often retrieves data in JSON format via APIs or embedded scripts, as it's lightweight and structured with key-value pairs, making it ideal for semi-structured data.
* CSV (Option B): CSV (Comma-Separated Values) is a structured format typically used for tabular data. It's not commonly the direct output of web scraping, though scraped data might be converted to CSV later.
* CSS (Option C): CSS (Cascading Style Sheets) is used for styling web pages and isn't a data format, making it irrelevant for describing scraped data.
* HTML (Option D): HTML (HyperText Markup Language) is the structure of web pages and is often the raw format scraped during web scraping. While HTML is semi-structured due to its tag-based hierarchy, it's primarily a markup language, not a data format, and the actual data extracted is often parsed into formats like JSON.
The DA0-002 Data Acquisition and Preparation domain aligns with the DA0-001 focus on "data acquisition concepts" (web ID: 14), which includes identifying formats like JSON as semi-structured data commonly acquired through web scraping. JSON is the best fit here due to its prevalence in web data exchange.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation
質問 # 28
Given the following table:
ID
Value
1
1.5
2
24.456
3
113
Which of the following data types should an analyst use for the numeric values in the Value column?
- A. Integer
- B. Boolean
- C. Double
- D. Float
正解：D
解説：
This question falls under theData Concepts and Environmentsdomain of CompTIA Data+ DA0-002, focusing on selecting appropriate data types for a given dataset. The Value column contains decimal numbers (1.5, 24.456, 113), requiring a data type that supports such values.
* Double (Option A): Double is a floating-point data type that supports decimals with higher precision than Float, but it's often overkill for typical datasets unless very high precision is needed, which isn't indicated here.
* Float (Option B): Float is a floating-point data type that supports decimal numbers (e.g., 1.5, 24.456) and is commonly used for such values in databases, making it the best choice.
* Boolean (Option C): Boolean is for true/false values, not numeric data.
* Integer (Option D): Integer is for whole numbers, but the values (e.g., 1.5, 24.456) have decimals, so Integer is not suitable.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like Float for decimal numeric values.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
質問 # 29
A data analyst deployed a report for public access. A user states that the report is not showing the latest information, even though the user updated the source an hour ago. Which of the following should the data analyst check first?
- A. Database connection
- B. Event log
- C. User privileges
- D. Report corruption
正解：A
解説：
This question pertains to theData Governancedomain, focusing on troubleshooting data freshness issues in reports. The report isn't showing the latest data despite a recent source update, indicating a potential refresh or connectivity issue.
* Event log (Option A): Event logs might provide insight into errors, but they're not the first step for checking data freshness.
* User privileges (Option B): Privileges might affect access, but the user can see the report, so this isn't the issue.
* Database connection (Option C): If the database connection failed or isn't refreshing properly, the report won't reflect the latest data, making this the first thing to check.
* Report corruption (Option D): Corruption might cause errors, but it's less likely than a connectivity issue for this scenario.
The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring data freshness by verifying database connections.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
質問 # 30
A data analyst receives the following sales data for a convenience store:
Item
Quantity
Price
Chocolate Bars
7
$1.99
Vanilla Ice Bars
2
$4.99
Chocolate Wafers
6
$0.99
Peanut Butter
2
$2.99
Cups
3
$4.99
Strawberry Jam
3
$4.99
Chocolate Cake
9
$6.99
Milk Chocolate
2
$2.99
Almonds
5
$2.99
The analyst needs to provide information on the products that contain chocolate. Which of the following RegEx should the analyst use to filter the chocolate products?
- A. Chocolate$
- B. Chocolate!
- C. #Chocolate#$
- D. %Chocolate&
正解：A
解説：
This question falls under theData Acquisition and Preparationdomain, which includes techniques for manipulating and filtering data, such as using regular expressions (RegEx) to identify specific patterns in text data. The task is to filter items containing the word "Chocolate."
* Chocolate! (Option A): In RegEx, "!" is not a valid pattern for matching a word like "Chocolate." It typically denotes negation in some contexts, but here it's incorrect.
* Chocolate$ (Option B): The "$" in RegEx anchors the pattern to the end of the string, meaning it matches "Chocolate" at the end of an item name (e.g., "Milk Chocolate"). This is the most appropriate pattern for identifying items ending with "Chocolate," which applies to the relevant items in the list.
* %Chocolate& (Option C): "%" and "&" are not standard RegEx anchors; they're often used in SQL LIKE patterns, not RegEx, making this incorrect.
* #Chocolate#$ (Option D): "#" is not a standard RegEx anchor, and this pattern would look for
"Chocolate" surrounded by "#", which doesn't match the data.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation" , and RegEx is a common technique for filtering text data. The pattern "Chocolate$" correctly identifies items like
"Chocolate Bars," "Chocolate Wafers," "Chocolate Cake," and "Milk Chocolate." Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation
質問 # 31
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