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CompTIA Data+ Exam (2025) Sample Questions (Q88-Q93):

NEW QUESTION # 88
Which of the following is a NoSQL database?

A. MySQL
B. MongoDB
C. PostgreSQL
D. Oracle

Answer: B

Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on types of databases. The task is to identify a NoSQL database among the options.
* PostgreSQL (Option A): PostgreSQL is a relational (SQL) database, not NoSQL.
* MySQL (Option B): MySQL is a relational (SQL) database, not NoSQL.
* Oracle (Option C): Oracle Database is a relational (SQL) database, not NoSQL.
* MongoDB (Option D): MongoDB is a NoSQL database that uses a document-based model, storing data in JSON-like structures, making it the correct choice.
The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and MongoDB is a well-known NoSQL database.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 89
A user needs a report that shows the main causes of customer churn rate in a three-year period. Which of the following methods provides this information?

A. Inferential
B. Predictive
C. Prescriptive
D. Descriptive

Answer: D

Explanation:
This question falls under theData Analysisdomain, focusing on analytical methods for reporting. The task is to identify the causes of customer churn over three years, which involves analyzing historical data.
* Inferential (Option A): Inferential statistics make predictions or generalizations about a population, not focused on identifying causes in historical data.
* Descriptive (Option B): Descriptive analytics summarizes historical data to identify patterns and causes (e.g., reasons for churn), which fits the task.
* Prescriptive (Option C): Prescriptive analytics provides recommendations, which goes beyond identifying causes.
* Predictive (Option D): Predictive analytics forecasts future outcomes, not focused on historical causes.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and descriptive analytics is best for identifying causes in historical data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

NEW QUESTION # 90
A table contains several rows of cellular numbers with call timestamps, call durations, called numbers, and carriers of the called number. Which of the following allows a data analyst to sort the cellular numbers based on the carriers of the called numbers and include the total call durations?

A. SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrier;
B. SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number, called_number_carrier ORDER BY called_number_carrier;
C. SELECT cellular_number, SUM(call_duration) FROM calls GROUP BY call_duration ORDERBY called_number_carrier;
D. SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number ORDER BY called_number_carrier;

Answer: B

Explanation:
This question falls under theData Analysisdomain of CompTIA Data+ DA0-002, focusing on SQL queries for data analysis. The task requires sorting cellular numbers by the carrier of the called number (called_number_carrier) and calculating the total call durations (SUM(call_duration)).
* Option A: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number ORDER BY called_number_carrierThis query groups by cellular_number only, but called_number_carrier is in the SELECT clause without being in the GROUP BY, which is invalid in SQL (it would raise an error in most databases).
* Option B: SELECT cellular_number, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrierThis query doesn't include called_number_carrier in the SELECT clause, so it cannot be used in the ORDER BY clause, making it invalid. Grouping by call_duration also doesn't align with the task.
* Option C: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number, called_number_carrier ORDER BY called_number_carrierThis query correctly groups by both cellular_number and called_number_carrier (since both are in the SELECT clause), calculates the total call duration with SUM(call_duration), and sorts by called_number_carrier as required.
* Option D: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrierGrouping by call_duration is incorrect because cellular_number and called_number_carrier are in the SELECT clause but not in the GROUP BY, making this query invalid.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and Option C correctly aggregates and sorts the data as specified.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

NEW QUESTION # 91
Which of the following is the most efficient to use when programming repeatable tasks?

A. NLP
B. Deep learning
C. LLM
D. RPA

Answer: D

Explanation:
This question pertains to theData Acquisition and Preparationdomain, focusing on tools for automating repeatable tasks in data processes. The task is to identify the most efficient programming method.
* LLM (Option A): Large Language Models (e.g., GPT) are for text generation, not efficient for repeatable data tasks.
* Deep learning (Option B): Deep learning is for complex pattern recognition (e.g., image classification), not efficient for simple repeatable tasks.
* NLP (Option C): Natural Language Processing is for text analysis, not general repeatable tasks.
* RPA (Option D): Robotic Process Automation (RPA) automates repetitive, rule-based tasks (e.g., data entry, file processing), making it the most efficient for programming repeatable tasks.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and RPA is a widely used method for automating repeatable data tasks.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.

NEW QUESTION # 92
Which of the following best explains the purpose of data lineage?

A. To track data transformations from acquisition through reporting
B. To better understand the granularity of data variable relationships
C. To look up data definitions, ensuring consistent use across business units
D. To see the steps and path of data flow through different systems

Answer: A

Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on the purpose of data lineage. Data lineage involves tracking the lifecycle of data.
* To see the steps and path of data flow through different systems (Option A): This describes a data flow diagram, not data lineage, which focuses on transformations rather than just flow.
* To better understand the granularity of data variable relationships (Option B): This relates to data modeling, not the purpose of data lineage.
* To track data transformations from acquisition through reporting (Option C): Data lineage tracks the journey of data, including transformations (e.g., cleaning, aggregation) from its source to its final use in reporting, which is its primary purpose.
* To look up data definitions, ensuring consistent use across business units (Option D): This describes a data dictionary, not data lineage.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and data lineage specifically tracks transformations across the data lifecycle.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 93
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