CompTIA DA0-002 Dumps - Try Free DA0-002 Exam Questions and Answer

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CompTIA DA0-002 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA Data+ Certification Exam
Exam Number:DA0-002
Certificate Validity Period:3 years
Passing Score:675 (on a scale of 100–900)
Exam Format:Multiple-choice, Performance-based questions, Multiple-response
Exam Price:$253 USD (may vary by region/tax)
Real Exam Qty:Up to 90
Exam Duration:90 minutes
Available Languages:English
Recommended Training:CompTIA CertMaster Learn for Data+
CompTIA Data+ Study Resources
Exam Registration:Pearson VUE CompTIA Exams Registration
CompTIA Data+ Certification Page
Sample Questions:CompTIA DA0-002 Sample Questions
Exam Way:Delivered via Pearson VUE testing centers or online proctored exam
Pre Condition:No mandatory prerequisites. Recommended: 18–24 months of experience in a business/data analytics environment.
Official Syllabus URL:https://www.comptia.org/certifications/data-plus

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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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.
Topic 4
  • 5.0 Data Governance, Quality, and Controls
Topic 5
  • This section of the exam measures skills of a Data Governance Associate and introduces principles for keeping data secure, accurate, and compliant. It covers access controls, encryption, classification of sensitive data like PII and PHI, and legal requirements tied to data use. Candidates must know how to apply quality checks, validate data, and manage master data effectively. It also addresses best practices for maintaining integrity through data dictionaries, audits, and standardisation processes.

CompTIA Data+ Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
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: B


NEW QUESTION # 153
A data analyst needs to modify a dashboard that was created by another employee. Upon opening the dashboard, the analyst notices that the information is not loading properly. Which of the following should the analyst do to troubleshoot this error?

Answer: A

Explanation:
When a dashboard fails to load information properly, the first step is to verify that the dashboard's data connections are functioning correctly. Reviewing the data layer and data source helps identify broken connections, unavailable sources, or data retrieval issues that commonly prevent dashboard content from loading.


NEW QUESTION # 154
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: A

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 # 155
A data analyst wants to analyze sales data for possible customer patterns. Which of the following should the analyst use to complete this task?

Answer: B

Explanation:
Clustering is a data analysis technique that groups customers or transactions with similar characteristics. By identifying these natural groupings within sales data, the analyst can discover customer patterns, behaviors, and trends that may not be immediately apparent.


NEW QUESTION # 156
A data analyst is working on an initial analysis of the dataset in the following table:

Which of the following issues should the analyst flag in the data report?

Answer: D

Explanation:
The value 245 is significantly higher than the other counts, indicating an outlier that should be flagged for further investigation.


NEW QUESTION # 157
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