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| Topic | Details |
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| Topic 1 | - Data Analysis: This section of the exam measures skills of a Reporting Analyst and includes foundational knowledge of statistical methods such as averages, variances, and standard deviation. It covers how to use data to find patterns, track performance, and make predictions. This domain also introduces hypothesis testing, regression, correlation, and different types of analysis like exploratory and trend analysis. Candidates should also be aware of common tools used for analysis, including Excel, SQL, Python, R, and popular BI platforms like Tableau and Power BI.
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| Topic 2 | - Data Mining: This section of the exam measures skills of a Business Intelligence Analyst and covers how data is collected, cleaned, and prepared for analysis. It explains methods like ETL and ELT for data integration, as well as web scraping, API use, and survey data collection. Candidates are expected to identify issues like missing or duplicated data and apply techniques like filtering, sorting, merging, and normalizing. The section also touches on query optimization strategies to improve data handling efficiency.
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| Topic 3 | - 5.0 Data Governance, Quality, and Controls
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| Topic 4 | - Visualization: This section of the exam measures skills of a Data Visualisation Specialist and focuses on turning raw data into clear, visual insights. It teaches how to match visual formats like bar charts, heat maps, and line graphs to specific audiences and needs. Candidates must understand how to create dashboards and reports using proper design elements such as labels, layout, branding, and colour schemes. This section also includes best practices for dashboard development and delivery through various platforms and user access levels.
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CompTIA Data+ Exam Sample Questions (Q39-Q44):
NEW QUESTION # 39
A company has a document that includes the names of key metrics and the standard for how those metrics are calculated company-wide. Which of the following describes this documentation?
- A. Data dictionary
- B. Data explainability report
- C. Data flow diagram
- D. Data lineage
Answer: A
Explanation:
This question falls under theData Concepts and Environmentsdomain, which involves understanding documentation types related to data management. The document describes key metrics and their calculation standards, which points to a specific type of metadata documentation.
* Data dictionary (Option A): A data dictionary defines data elements, including metrics, their meanings, and calculation methods, ensuring consistency across the organization. This matches the description.
* Data explainability report (Option B): This term is more associated with AI/ML, explaining model decisions, not metric definitions.
* Data lineage (Option C): Data lineage tracks the flow of data through systems, not metric definitions or calculations.
* Data flow diagram (Option D): A data flow diagram visualizes data processes, not metric standards.
The DA0-002 Data Concepts and Environments domain includes understanding "basic concepts of data schemas and dimensions" , and a data dictionary is a foundational tool for defining metrics.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments
NEW QUESTION # 40
A data analyst creates a report that identifies the middle 50% of the collected data. Which of the following best describes the analyst's findings?
- A. Mean variance
- B. Skewness from the slope
- C. The difference between mode and median
- D. Interquartile range
Answer: D
Explanation:
The interquartile range (IQR) represents the spread of the middle 50% of a dataset. It is calculated as the difference between the third quartile (75th percentile) and the first quartile (25th percentile), making it a useful measure of variability that is less affected by outliers.
NEW QUESTION # 41
A company's analytics manager wants all reports to be delivered once every seven days. Which of the following is the best delivery method?
- A. Ad hoc
- B. Recurring
- C. Snapshot
- D. Custom
Answer: B
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on report delivery methods. The requirement for delivery every seven days indicates a scheduled, repeating process.
* Recurring (Option A): Recurring delivery schedules reports to be generated and delivered at regular intervals (e.g., weekly), which matches the requirement of every seven days.
* Ad hoc (Option B): Ad hoc reports are one-time, on-demand reports, not suitable for scheduled delivery.
* Custom (Option C): Custom isn't a standard delivery method; it might refer to tailored reports but doesn't imply scheduling.
* Snapshot (Option D): A snapshot captures data at a specific point, not suitable for recurring delivery.
The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and recurring delivery is ideal for weekly reports.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 42
A data analyst is creating a report that provides information to parents and residents about the school distinct and its individual schools. Which of the following statistical methods is best suited for reporting this information?
- A. Predictive
- B. Prescriptive
- C. Descriptive
- D. Inferential
Answer: C
Explanation:
Descriptive statistics are used to summarize and present existing data in a meaningful way. Since the report is intended to provide information about the school district and its individual schools, descriptive methods are best suited to present current facts, metrics, and characteristics without making predictions or drawing conclusions about a larger population.
NEW QUESTION # 43
Which of the following is the most efficient to use when programming repeatable tasks?
- A. RPA
- B. NLP
- C. Deep learning
- D. LLM
Answer: A
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 # 44
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