CompTIA DA0-001 - CompTIA Data+ Certification Exam

Gross profit percentage is a key performance indicator (KPI) that measures the profitability of a company’s sales by showing the percentage of revenue that exceeds the cost of goods sold (COGS). It is a critical metric for tracking sales performance because it directly reflects the efficiency of a company in managing its production costs and the profitability of its products. This KPI is essential for understanding the financial health of a business and making informed decisions about pricing, cost control, and sales strategies.

The correct answer is B. Recoding.

Recoding is a data manipulation technique that involves changing the values or categories of a variable to make it more suitable for analysis.Recoding can be used to simplify or group the data, to correct errors or inconsistencies, or to create new variables from existing ones12

In the example, the analyst used recoding to change the values of Var001, Var002, Var003, and Var004 from numerical to textual form. The analyst also used recoding to assign meaningful labels to the values, such as “Absent” for 0, “Present” for 1, “Low” for 2, “Medium” for 3, and “High” for 4. This makes the data more understandable and easier to analyze.

Explanation

Correct answer B. When data is at rest or in transit.

To provide maximum protection, encrypt data both in transit and at rest.

 A Type II error is a false negative conclusion, which means failing to reject a null hypothesis that is actually false. In the table, box 3 indicates that a Type II error has occurred, because it shows that the null hypothesis is accepted when it is false in reality.This means that the statistical test failed to detect a significant difference or relationship that actually exists. References: Type I & Type II Errors | Differences, Examples, Visualizations - Scribbr, Type I and type II errors - Wikipedia

Getting stakeholder approval is the next step the analyst should take in the dashboard creation process, after confirming the data sources and creating a wireframe. Stakeholder approval means getting feedback and validation from the intended users or clients of the dashboard, to ensure that it meets their expectations and requirements. This step helps to avoid rework and ensure customer satisfaction. References: CompTIA Data+ Certification Exam Objectives, page 14

The Chi-squared test is used to analyze relationships between two categorical variables. In this case, age groups and voting preferences are both categorical variables, making chi-squared the most appropriate test​.

To efficiently meet the client's requirements of providing both regional and global sales data to all sales teams, it's essential to design a reporting solution that is both scalable and user-friendly.

Option A:Creating a single report with a region filter

Rationale:Developing a single report that includes a dynamic region filter allows users to select their specific region or view combined sales data as needed. This approach ensures that all users access the same report, reducing redundancy and maintenance efforts. It also empowers users to customize their view according to their needs without the need for multiple reports.

Exploratory Data Analysis (EDA) is an approach used by analysts to analyze data sets to summarize their main characteristics, often employing visual methods. The primary goal of EDA is to discover patterns, spot anomalies, test hypotheses, and check assumptions through statistical graphics and other data visualization tools.

Option A:Performance analysis

Rationale:Performance analysis focuses on assessing the efficiency and effectiveness of a system, process, or individual. It involves measuring key performance indicators (KPIs) to determine how well objectives are being met. This type of analysis is not primarily concerned with identifying patterns or anomalies in data sets.

Option B:Exploratory analysis

Rationale:Exploratory analysis involves examining data sets to uncover underlying structures, extract important variables, detect outliers, and test underlying assumptions. By using statistical tools and visualization techniques, analysts can identify patterns andanomalies, making it a crucial first step in data analysis.

Here, the problem is about granularity and detail level of the data:

The organization only captures sales by region, not by store.

This means certain attributes (e.g., store identifier) are not present or not captured at the required level of detail.

In CompTIA Data+ language, this is a case of data attribute limitations:

The data does not include all the necessary attributes (or the required level of those attributes) to perform finer-grained analysis (e.g., store-level performance, store comparisons).

Why the other options are incorrect:

Data accuracy (B): Deals with whether data values are correct/true; here, the issue is not that regional sales are wrong, but that they lack detail.

Data integrity (C): Refers to internal correctness and reliability (e.g., foreign keys, referential integrity, completeness of relationships).

Data consistency (D): Concerns whether data is uniform across systems or over time (e.g., same format, same definitions).

The main issue is not enough attributes / insufficient granularity, which matches Data attribute limitations (A).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Data quality: completeness, granularity, and attribute sufficiency.

CompTIA Data+ Study Guide – discussions on data attribute limitations and granularity impacting the types of analysis that can be performed.