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CompTIA Data+ Exam (2025) Sample Questions (Q91-Q96):

NEW QUESTION # 91
A data analyst must combine service calls into low-, medium-, and high-priority levels in order to analyze organizational responses. Which of the following techniques should the analyst use for this task?

• A. Imputation
• B. Augmentation
• C. Scaling
• D. Binning

Answer: D

Explanation:
This question pertains to theData Analysisdomain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.
* Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.
* Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.
* Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.
* Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a standard technique for categorizing data for analysis.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

NEW QUESTION # 92
Which of the following is the most efficient to use when programming repeatable tasks?

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

Answer: B

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 # 93
A recent server migration applied an update to dataset naming conventions. Multiple users are now reporting stale information in an existing dashboard. The date in the dataset confirms a successful data refresh. Which of the following should a data analyst do first?

• A. Verify that the dashboard subscription is not expired.
• B. Filter the data in the dashboard.
• C. Confirm the dashboard is pointed to the newest dataset.
• D. Escalate user permissions on the server.

Answer: C

Explanation:
This question falls under theData Governancedomain, focusing on troubleshooting data freshness issues in dashboards. The dashboard shows stale data despite a successful refresh, and the server migration updated naming conventions, suggesting a potential mismatch.
* Confirm the dashboard is pointed to the newest dataset (Option A): The server migration updated dataset naming conventions, so the dashboard might still be pointing to an old dataset name, causing stale data. Confirming the dataset connection is the first step.
* Filter the data in the dashboard (Option B): Filtering might adjust the view but doesn't address the root cause of stale data.
* Escalate user permissions on the server (Option C): Permissions issues would likely prevent access, not cause stale data, especially since the dataset refreshed successfully.
* Verify that the dashboard subscription is not expired (Option D): An expired subscription might prevent access, but the dashboard is accessible, just showing stale data.
The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring dashboards connect to the correct, updated datasets after changes like server migrations.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

NEW QUESTION # 94
A data analyst needs to get an accurate idea of how data components are automated. Which of the following types of documentation should the analyst review first?

• A. Data dictionary
• B. Data flow diagram
• C. Data explainability report
• D. Data lineage

Answer: B

Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on documentation for understanding data processes. The analyst needs to understand automation of data components, which involves data movement and processes.
* Data flow diagram (Option A): A data flow diagram (DFD) visualizes how data moves through systems, including automated processes, making it the best starting point.
* Data explainability report (Option B): This is related to AI/ML model transparency, not data automation.
* Data dictionary (Option C): A data dictionary defines data elements, not how they're automated.
* Data lineage (Option D): Data lineage tracks data origin and transformations but doesn't focus on automation processes.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data flow diagram is key for visualizing automation.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

NEW QUESTION # 95
A data analyst is preparing a survey for Paralympic Games athletes. Which of the following should the analyst consider when creating this survey?

• A. Granularity
• B. Refresh speed
• C. Color contrast
• D. Idioms

Answer: C

Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on survey design considerations, particularly for accessibility. The survey is for Paralympic athletes, who may have visual impairments, requiring specific design considerations.
* Idioms (Option A): Idioms (e.g., colloquial phrases) might confuse non-native speakers, but they're not a primary survey design concern for Paralympic athletes.
* Color contrast (Option B): High color contrast ensures readability for athletes with visual impairments (e.g., color blindness), a critical accessibility consideration for Paralympic surveys.
* Refresh speed (Option C): Refresh speed is relevant for dashboards, not static surveys.
* Granularity (Option D): Granularity refers to data detail levels, not a survey design consideration.
The DA0-002 Visualization and Reporting domain includes "translating business requirements to form the appropriate visualization," and color contrast is a key accessibility factor in survey design for diverse audiences.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.

NEW QUESTION # 96
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