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NEW QUESTION # 49
A data analyst is analyzing the following dataset:
Transaction Date
Quantity
Item
Item Price
12/12/12
11
USB Cords
9.99
11/11/11
3
Charging Block
8.89
10/10/10
5
Headphones
50.15
Which of the following methods should the analyst use to determine the total cost for each transaction?
Answer: B
Explanation:
This question falls under theData Analysisdomain, focusing on calculating new values from existing data.
The task is to determine the total cost per transaction, which involves multiplying Quantity by Item Price.
* Parsing (Option A): Parsing involves breaking down data (e.g., splitting a string), not calculating totals.
* Scaling (Option B): Scaling adjusts numerical values to a common range (e.g., normalization), not relevant for calculating totals.
* Compressing (Option C): Compressing reduces data size, not applicable to calculating costs.
* Deriving (Option D): Deriving involves creating new data fields by performing calculations on existing ones (e.g., Total Cost = Quantity × Item Price), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as deriving new fields through calculations to analyze data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 50
A project manager requests an unscheduled report that provides a list of clients. Which of the following frequencies is best for this report?
Answer: B
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on report delivery frequencies.
The report is described as unscheduled, meaning it's a one-time request.
* Annual (Option A): Annual frequency implies a scheduled report every year, not suitable for an unscheduled request.
* Daily (Option B): Daily frequency implies a scheduled report each day, not suitable.
* Weekly (Option C): Weekly frequency implies a scheduled report each week, not suitable.
* Ad hoc (Option D): Ad hoc reports are generated on-demand for one-time or unscheduledneeds, which matches the project manager's request.
The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and ad hoc is the best frequency for an unscheduled report.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 51
A report triggers an error that prevents information from being displayed. However, the report was functional before a database upgrade. Which of the following should a data analyst do first to troubleshoot the problem?
Answer: D
Explanation:
This question falls under theData Concepts and Environmentsdomain, which in DA0-002 involves understanding database environments, connections, and troubleshooting issues related to data access. The scenario describes a report failing after a database upgrade, indicating a potential issue with the database environment or connectivity.
* Ensure the system has permissions for the report service (Option A): While permissions issues can cause report failures, they are less likely to be the first issue after a database upgrade unless explicitly mentioned.
* Change the report's refresh rate (Option B): Refresh rate adjustments might address performance issues but won't resolve a fundamental error preventing data display.
* Verify the connection to the database (Option C): A database upgrade often involves changes to connection strings, drivers, or network configurations. Verifying the connection ensures the report can access the database, making this the most logical first step.
* Check whether the data structures were modified (Option D): While possible, checking data structures (e.g., schema changes) is a deeper troubleshooting step that should follow after confirming basic connectivity.
The DA0-002 Data Concepts and Environments domain includes understanding database connectivity, similar to DA0-001's focus on "data schemas and dimensions" and environments (web ID: 1). Verifying the connection is the first recommended step in troubleshooting post-upgrade issues.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments
NEW QUESTION # 52
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?
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 # 53
A data analyst receives four files that need to be unified into a single spreadsheet for further analysis. All of the files have the same structure, number of columns, and field names, but each file contains different values.
Which of the following methods will help the analyst convert the files into a single spreadsheet?
Answer: A
Explanation:
This question is part of theData Acquisition and Preparationdomain, which involves combining data from multiple sources. The files have the same structure but different values, meaning theyneed to be stacked vertically into one dataset.
* Merging (Option A): Merging typically involves joining datasets on a common key (e.g., a customer ID), which isn't indicated here since the files only differ in values, not keys.
* Appending (Option B): Appending stacks datasets vertically, combining rows from files with the same structure into a single dataset, which matches the scenario.
* Parsing (Option C): Parsing involves breaking down data (e.g., splitting text), not combining files.
* Clustering (Option D): Clustering is a machine learning technique for grouping similar data points, not for combining files.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as appending datasets with identical structures.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
NEW QUESTION # 54
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