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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 5.0 Data Governance, Quality, and Controls
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
  • 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 4
  • Data Concepts and Environments: This section of the exam measures the skills of a Junior Data Analyst and focuses on understanding core data concepts such as database types, schema structures, and data formats. It highlights differences between structured and unstructured data, compares file types like CSV, JSON, and XML, and introduces key ideas about data dimensions and slowly changing dimensions. Knowing how data is stored and organized helps professionals better prepare for analysis and reporting tasks.

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CompTIA Data+ Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
Which of the following data structures would a data engineer most likely receive when using a get command?

Answer: C

Explanation:
JSON (JavaScript Object Notation) is a common data structure returned by GET requests in APIs and web services. It provides a lightweight, structured format for exchanging data between systems and is widely used for transmitting records, objects, and arrays.


NEW QUESTION # 59
A data analyst is cleaning up a data set that contains images. The analysts wants to keep everything that was produced within the last 12 months. Which of the following best describes where the analyst should check for the date of creation on these products?

Answer: B

Explanation:
Metadata contains descriptive information about each file or image, including attributes such as creation date, modification date, file size, and format. To determine which images were produced within the last 12 months, the analyst should examine the metadata of each item rather than the creation date of the dataset or folder containing the files.


NEW QUESTION # 60
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?

Answer: B

Explanation:
A high-level email provides a concise summary of key sales metrics, which aligns with executive needs for quick, decision-focused insights without excessive detail.


NEW QUESTION # 61
A company wants to limit an employee's access to a production environment. Which of the following access control practices is the best to implement?

Answer: C


NEW QUESTION # 62
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: B

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 # 63
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