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CompTIA DA0-001 Exam is a certification exam designed to test an individual's knowledge of data management and analysis. DA0-001 exam is intended for professionals who are seeking to validate their understanding of data storage, processing, and analysis in order to advance their careers in the field of data management.

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CompTIA Data+ Certification Exam Sample Questions (Q140-Q145):

NEW QUESTION # 140
You are looking at a dashboard is sales by a US state and would like to see the sales for individual counties within a specific state What action should you take?

A. Drill down.
B. Roll up.
C. Deviate.
D. Aggregate.

Answer: A

NEW QUESTION # 141
Which one the following is not considered an aggregate function?

A. MAX
B. SELECT
C. SUM
D. MIN

Answer: B

NEW QUESTION # 142
Which of following is a non-relational database?

A. SQLite
B. MySQL
C. PostgreSQL
D. Neo4j

Answer: D

Explanation:
Explanation
Neo4j is a type of non-relational database that uses a graph model to store data. A graph database is a database that represents data as nodes and edges, where nodes are entities and edges are relationships between them. A graph database can store complex and diverse data that is not easily structured in tables. A graph database can also perform fast and efficient queries on the data by traversing the connections between the nodes

NEW QUESTION # 143
An analyst has been asked to validate data quality. Which of the following are the BEST reasons to validate data for quality control purposes? (Choose two.)

A. Encryption
B. Consistency
C. Transmission
D. Retention
E. Deletion
F. Integrity

Answer: F

Explanation:
Integrity and D. Consistency. This is because integrity and consistency are two of the best reasons to validate data for quality control purposes, which means to check and ensure that the data is accurate, complete, reliable, and usable for the intended analysis or purpose. By validating data for integrity and consistency, the analyst can prevent or correct any errors or issues in the data that could affect the validity or reliability of the analysis or the results. Here is what integrity and consistency mean in terms of data quality:
Integrity refers to the completeness and validity of the data, which means that the data has no missing, incomplete, or invalid values that could compromise its meaning or usefulness. For example, validating data for integrity could involve checking for null values, outliers, or incorrect data types in the data set.
Consistency refers to the uniformity and standardization of the data, which means that the data follows a common format, structure, or rule across different sources or systems. For example, validating data for consistency could involve checking for spelling, punctuation, or capitalization errors in the data set.
The other reasons are not the best reasons to validate data for quality control purposes. Here is why:
Retention refers to the storage and preservation of the data, which means that the data is kept and maintained in a secure and accessible way for future use or reference. Retention does not need to be validated for quality control purposes, because it does not affect the accuracy or reliability of the data itself.
Transmission refers to the transfer and exchange of the data, which means that the data is moved or shared between different sources or systems in a fast and efficient way. Transmission does not need to be validated for quality control purposes, because it does not affect the completeness or validity of the data itself.
Encryption refers to the protection and security of the data, which means that the data is encoded or scrambled in a way that prevents unauthorized access or use. Encryption does not need to be validated for quality control purposes, because it does not affect the uniformity or standardization of the data itself.
Deletion refers to the removal and disposal of the data, which means that the data is erased or destroyed in a way that prevents recovery or retrieval. Deletion does not need to be validated for quality control purposes, because it does not affect the meaning or usefulness of the data itself.

NEW QUESTION # 144
Which of the following should an analyst do to best summarize the data on a data set?

A. Filtering
B. Sorting
C. Aggregation
D. Concatenation

Answer: C

NEW QUESTION # 145
......

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