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CompTIA Data+ Exam (2025) Sample Questions (Q11-Q16):

NEW QUESTION # 11
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?

A. Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together.
B. Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together.
C. Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together.
D. Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together.

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 # 12
A data analyst is creating a new dataset that involves bringing together the following datasets:
Name
ID
Date of birth
Frank
23525
3/19
Martha
11290
6/13
Ellen
12141
11/4
ID
Address
City
State
23525
1234 Harding
Chicago
IL
11040
935 Terrace Hills
Chino
CA
11290
2 Speedway
Miami
FL
Which of the following would be the output if the data analyst does a FULL JOIN?

A. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
935 Terrace Hills
Chino
CA
Ellen
12141
11/4
2 Speedway
Miami
FL
B. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
2 Speedway
Miami
FL
Ellen
12141
11/4
935 Terrace Hills
Chino
CA
C. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
2 Speedway
Miami
FL
Ellen
12141
11/4
11040
935 Terrace Hills
Chino
CA
D. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
935 Terrace Hills
Chino
CA
Ellen
12141
11/4
2 Speedway
Miami
FL

Answer: C

Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on database operations like joins. A FULL JOIN combines all rows from both tables, including matches and non-matches, filling in NULLs where there's no corresponding data.
* The first table has IDs: 23525 (Frank), 11290 (Martha), 12141 (Ellen).
* The second table has IDs: 23525, 11040, 11290.
* A FULL JOIN includes all IDs: 23525, 11290, 12141, 11040.
* 23525 matches (Frank with 1234 Harding, Chicago, IL).
* 11290 matches (Martha with 2 Speedway, Miami, FL).
* 12141 has no match in the second table, so Address, City, and State are NULL.
* 11040 has no match in the first table, so Name and Date of birth are NULL.
* Option A: Incorrect; it includes a row for Ellen with "2 Speedway," but Ellen's ID (12141) doesn't match any address, and 11040 is missing.
* Option B: Identical to Option A, so incorrect for the same reasons.
* Option C: Incorrect; it mismatches addresses (e.g., Ellen with 935 Terrace Hills, which belongs to
11040).
* Option D: Correct; it includes all IDs, with NULLs for non-matching rows (Ellen has no address, and
11040 has no name).
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as performing joins in relational databases.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 13
A data analyst needs to create a combined report that includes information from the following two tables:
Managers table
ID
First_name
Last_name
Job_title
1001
John
Doe
Manager
1002
Jane
Roe
Director
Non-managers table
ID
First_name
Last_name
Job_title
1003
Robert
Roe
Business Analyst
1004
Jane
Doe
Sales Representative
1005
John
Roe
Operations Analyst
Which of the following query methods should the analyst use for this task?

A. Join
B. Nested
C. Group
D. Union

Answer: D

Explanation:
This question pertains to theData Acquisition and Preparationdomain, focusing on combining data from two tables. Both tables have the same structure (ID, First_name, Last_name, Job_title) and contain employee data, suggesting the task is to create a single list of all employees.
* Group (Option A): Grouping (e.g., GROUP BY in SQL) is for aggregation (e.g., counting employees by job title), not combining tables into a single report.
* Join (Option B): Joining tables (e.g., INNER JOIN) requires a common key and combines tables horizontally, but there's no indication of a relationship between the tables (e.g., no shared key beyond ID, which isn't linked).
* Union (Option C): UNION combines the rows of two tables with the same structure into a single result set, removing duplicates, which is ideal for creating a combined report of all employees from both tables.
* Nested (Option D): Nested queries (e.g., subqueries) are used for complex filtering, not for combining tables into a single list.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and UNION is the best method for combining two tables with identical structures into a single report.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.

NEW QUESTION # 14
Which of the following data repositories stores unformatted data in its original, raw form?

A. Data warehouse
B. Data silo
C. Data mart
D. Data lake

Answer: D

Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on data repositories. The task is to identify a repository that stores raw, unformatted data.
* Data warehouse (Option A): A data warehouse stores structured, processed data in a predefined schema, not raw data.
* Data silo (Option B): A data silo is an isolated repository, often structured, not designed for raw data storage.
* Data mart (Option C): A data mart is a subset of a data warehouse, also storing structured data.
* Data lake (Option D): A data lake stores raw, unformatted data in its original format(structured, semi- structured, or unstructured), making it the correct choice.
The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed for raw data storage.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 15
Which of the following best explains the purpose of data lineage?

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

Answer: B

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 # 16
......

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