Valid CompTIA DA0-002 Test Dumps | DA0-002 Questions Pdf

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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Mining: This section of the exam measures skills of a Business Intelligence Analyst and covers how data is collected, cleaned, and prepared for analysis. It explains methods like ETL and ELT for data integration, as well as web scraping, API use, and survey data collection. Candidates are expected to identify issues like missing or duplicated data and apply techniques like filtering, sorting, merging, and normalizing. The section also touches on query optimization strategies to improve data handling efficiency.
Topic 2
  • Data Analysis: This section of the exam measures skills of a Reporting Analyst and includes foundational knowledge of statistical methods such as averages, variances, and standard deviation. It covers how to use data to find patterns, track performance, and make predictions. This domain also introduces hypothesis testing, regression, correlation, and different types of analysis like exploratory and trend analysis. Candidates should also be aware of common tools used for analysis, including Excel, SQL, Python, R, and popular BI platforms like Tableau and Power BI.
Topic 3
  • Visualization: This section of the exam measures skills of a Data Visualisation Specialist and focuses on turning raw data into clear, visual insights. It teaches how to match visual formats like bar charts, heat maps, and line graphs to specific audiences and needs. Candidates must understand how to create dashboards and reports using proper design elements such as labels, layout, branding, and colour schemes. This section also includes best practices for dashboard development and delivery through various platforms and user access levels.

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CompTIA Data+ Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
Which of the following best describes the semi-structured data that is gathered when web scraping?

Answer: B

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 # 132
A data analyst is creating a user sentiment analysis for a new product. Which of the following is the best method to acquire the data?

Answer: D

Explanation:
Surveying customers is the best method for gathering user sentiment data because it collects direct feedback about opinions, attitudes, and experiences with the product. This provides the qualitative and quantitative information needed to assess customer sentiment accurately.


NEW QUESTION # 133
A data analyst is creating a dashboard to monitor and manage processes on a daily, weekly, and quarterly basis. Which of the following types of dashboards is the best for this purpose?

Answer: D

Explanation:
Tactical dashboards focus on monitoring and managing processes over medium-term periods like daily, weekly, and quarterly, supporting mid-level management decisions.


NEW QUESTION # 134
A data analyst needs to identify outliers from a given data set. Which of the following visualizations is the best way to identify outliers?

Answer: B

Explanation:
A box plot is specifically designed to display the distribution of data and highlight outliers. Values that fall significantly above or below the typical range are shown separately from the whiskers, making outliers easy to identify.


NEW QUESTION # 135
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?

Answer: D

Explanation:
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.


NEW QUESTION # 136
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