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| Section | Weight | Objectives |
|---|---|---|
| Data Governance, Quality, and Management | 14% | - Data privacy, security, and compliance - Data quality standards and monitoring - Data lifecycle management and documentation - Data governance frameworks and policies |
| Data Acquisition and Preparation | 22% | - Data extraction, transformation, and loading (ETL/ELT) - Data cleaning, validation, and quality improvement - Data collection methods and sources - Data integration and normalization |
| Data Analysis | 24% | - Analytical tools and querying languages - Data modeling and pattern recognition - Exploratory and confirmatory data analysis - Statistical methods and techniques |
| Visualization and Reporting | 20% | - Dashboard and report development - Design principles for effective visualizations - Communicating insights to stakeholders - Validation and accuracy of reports |
| Data Concepts and Environments | 20% | - Foundational concepts of AI and machine learning in data - Cloud and distributed data environments - Data types, structures, and formats - Database systems and architectures |
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NEW QUESTION # 30
Given the following tables:
Which of the following data privacy practices is being used to protect employees' email addresses when creating a report?
Answer: D
Explanation:
Masking hides sensitive data by replacing portions of the original value with obscured characters, such as replacing email addresses with "xxxxx@company.com" while preserving the format.
NEW QUESTION # 31
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?
Answer: B
NEW QUESTION # 32
A sales analyst is using the following table to produce a report on January sales data:
Month
Sales
Year
Product
Jan
4324563
2024
Chair
Jan
23345
2024
Table
Jan
2456897
2024
Sofa
Jan
345456
2024
Desk
Jan
23345
2024
Table
Which of the following inconsistencies is present in the data set?
Answer: D
NEW QUESTION # 33
A data analyst wants to use the following tables to find all the customers who have not placed an order:
Customers table
ID
Name
Address
Products table
ID
Name
Customer_ID
Which of the following SQL statements is the best way to accomplish this task?
Answer: C
Explanation:
This question pertains to theData Analysisdomain, focusing on SQL queries to analyze data relationships.
The task is to find customers who have not placed an order, meaning customers in the Customers table without a matching Customer_ID in the Products table.
* Option A: SELECT * FROM CUSTOMERS AS C LEFT JOIN PRODUCTS AS P ON C.ID = P.
Customer_ID WHERE P.Customer_ID IS NULLA LEFT JOIN includes all customers, even those without orders (where Products columns are NULL). Filtering with WHERE P.Customer_ID IS NULL selects only customers without a match in Products, correctly identifying those who haven't ordered.
* Option B: SELECT * FROM CUSTOMERS AS C INNER JOIN PRODUCTS AS P ON C.ID = C.
ID WHERE COUNT(P.*) = 0An INNER JOIN only includes matching records, so it won't return customers without orders. The join condition C.ID = C.ID is also incorrect, and COUNT requires a GROUP BY, making this invalid.
* Option C: SELECT * FROM PRODUCTS AS P INNER JOIN CUSTOMERS AS C ON P.
Customer_ID = C.ID WHERE (SELECT COUNT(P.*) = 0)An INNER JOIN excludes customers without orders, and the subquery syntax is incorrect (COUNT needs a GROUP BY or to be part of a HAVING clause).
* Option D: SELECT * FROM PRODUCTS AS P LEFT JOIN CUSTOMERS AS C ON P.
Customer_ID = C.ID WHERE P.Customer_ID IS NOT NULLThis starts with Products and joins Customers, returning only records with orders (opposite of the task), and IS NOT NULL further excludes non-ordering customers.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and a LEFT JOIN with a NULL check is the standard method for finding non-matching records.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 34
Which of the following best describes the semi-structured data that is gathered when web scraping?
Answer: A
Explanation:
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
NEW QUESTION # 35
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