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CompTIA DA0-001 Exam Questions

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Description

Exam Name: CompTIA Data+ Certification Exam
Exam Code: DA0-001
Related Certification(s): CompTIA Data+ Certification
Certification Provider: CompTIA
Number of DA0-001 practice questions in our database: 314 (updated: Jan. 07, 2025)
Expected DA0-001 Exam Topics, as suggested by CompTIA :

  • Module 1: Data Concepts and Environments: This topic covers fundamental concepts related to data schemas, dimensions, and data types, along with a comparison of common data structures and file formats.
  • Module 2: Data Mining: In this topic, you’ll learn about data acquisition, the importance of data cleansing and profiling, techniques for data manipulation and query optimization to prepare data for analysis.
  • Module 3: Data Analysis: Here, the focus is on applying descriptive statistical methods to analyze data in scenarios, understanding inferential statistical methods, and exploring different types of analysis techniques along with tools commonly used in data analytics.
  • Module 4: Visualization: This topic explores translating business requirements into report formats, designing effective reports and dashboards using appropriate components, methods for dashboard development, and selecting suitable visualization types.
  • Module 5: Data Governance, Quality, and Controls: It covers essential concepts of data governance, applying data quality control techniques in scenarios, and understanding master data management (MDM) principles.

Description

Exam Name: CompTIA Data+ Certification Exam
Exam Code: DA0-001
Related Certification(s): CompTIA Data+ Certification
Certification Provider: CompTIA
Number of DA0-001 practice questions in our database: 314 (updated: Jan. 07, 2025)
Expected DA0-001 Exam Topics, as suggested by CompTIA :

  • Module 1: Data Concepts and Environments: This topic covers fundamental concepts related to data schemas, dimensions, and data types, along with a comparison of common data structures and file formats.
  • Module 2: Data Mining: In this topic, you’ll learn about data acquisition, the importance of data cleansing and profiling, techniques for data manipulation and query optimization to prepare data for analysis.
  • Module 3: Data Analysis: Here, the focus is on applying descriptive statistical methods to analyze data in scenarios, understanding inferential statistical methods, and exploring different types of analysis techniques along with tools commonly used in data analytics.
  • Module 4: Visualization: This topic explores translating business requirements into report formats, designing effective reports and dashboards using appropriate components, methods for dashboard development, and selecting suitable visualization types.
  • Module 5: Data Governance, Quality, and Controls: It covers essential concepts of data governance, applying data quality control techniques in scenarios, and understanding master data management (MDM) principles.

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Q1. Which of the following data manipulation techniques should an analyst use to hide unnecessary data during analysis?

A.Filtering

B. Parametrization

C. Sorting

D. Indexing

Correct Answer: A

Q2. Which of the following data protection methods provides confidentiality for data in transit?

A.De-identification

B. Encryption

C. Masking

D. Anonymization

Correct Answer: B

Q3. A data analyst needs to write a SOL query measuring last month's website visits and distribute a summary report to the marketing team. Which of the following is the analyst creating?

A.Date range

B. Distribution list

C. Data content

D. Report view

Correct Answer: D

Q4. Which of the following is the most likely reason for a data analyst to optimize a query using parameterization?

A.To return a subset of records

B. To insert a temporary table

C. To prevent SQL injections

D. To increase the query speed

Correct Answer: C

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