2019 goal data
To report how the sales team is performing to target, the sales analyst needs to compare the actual sales revenue with the expected or planned sales revenue for the same period. The 2019 goal data is the file that contains the expected or planned sales revenue for the year 2019, which is the target that the sales team is aiming to achieve. By comparing the 2019 goal data with the 2019 actual revenue, the sales analyst can calculate the performance attainment, which is the percentage of the goal that was met by the sales team.
Option A is incorrect, as 2018 goal data is not relevant for determining 2019 performance attainment. The
2018 goal data contains the expected or planned sales revenue for the year 2018, which is not the target that the sales team is aiming to achieve in 2019.
Option B is incorrect, as 2018 actual revenue is not relevant for determining 2019 performance attainment.
The 2018 actual revenue contains the actual sales revenue for the year 2018, which is not comparable with the
2019 goal data or the 2019 actual revenue.
Option D is incorrect, as 2019 commission plan is not relevant for determining 2019 performance attainment.
The 2019 commission plan contains the rules and rates for calculating and paying commissions to the sales team based on their performance attainment, but it does not contain the expected or planned sales revenue for the year 2019.

After writing the report, the most appropriate next step for the analyst is to complete a check for quality in the report. This involves reviewing the report for accuracy, clarity, completeness, consistency, and relevance. The analyst should ensure that the report addresses the director's business questions and objectives, that the data and analysis are correct and reliable, that the tables and graphs are well-designed and easy to understand, that the descriptions and summary are concise and informative, and that there are no errors or inconsistencies in the report. A quality check will help the analyst to improve the presentation and communication of the report, as well as to avoid any misunderstandings or misinterpretations by the director1.

This report contains high-level, strategic sales metrics:
* Total dollars closed for the year - top-level performance outcome.
* Annual quota/goal - alignment against strategic targets.
* Top 10 customers - key accounts, often central to executive focus.
* Average deal size and largest deals lost - insight into deal quality and major wins/losses.
CompTIA Data+ associates these kinds of summary KPIs and strategic metrics with executive-level / C-suite audiences, who:
* Need concise, high-level views rather than operational details.
* Focus on revenue performance, strategic accounts, and major risks/opportunities.
Why other options are less appropriate:
* External vendors (A) - usually don't receive internal detailed revenue performance reports.
* General public (B) - these are internal performance metrics, rarely shared publicly.
* Lower-level managers (C) - they typically require more operational/segment/store-level detail, not just top 10 customers or largest deals lost at a global level.
Therefore, this summary-style, executive KPI report is most appropriate for C-suite officers (D).
CompTIA Data+ Reference (concept alignment):
* DA0-001 Objectives - Reporting: tailoring reports and dashboards for audience and level (operational vs executive).
* Study content on executive dashboards and high-level KPI reporting.

Exploratory Data Analysis (EDA) is a technique used to summarize the main characteristics of a dataset. It often involves using graphical representations and statistical summaries to uncover patterns, relationships, and anomalies in the data. Analysts perform EDA to detect outliers, missing values, and trends before proceeding with deeper data processing and modeling.
Reference: CompTIA Data+ DA0-001 Official Guide

The correct answer is B. Snowflake.
A snowflake schema is a type of database schema that features normalized dimension tables. A database schema is a way of organizing and structuring the data in a database. A dimension table is a table that contains descriptive attributes or characteristics of the data, such as product name, category, color, etc. A normalized table is a table that follows the rules of normalization, which is a process of reducing data redundancy and improving data integrity by organizing the data into smaller and simpler tables12 A snowflake schema is a variation of the star schema, which is another type of database schema that features denormalized dimension tables. A denormalized table is a table that does not follow the rules of normalization, and may contain redundant or duplicated dat a. A star schema consists of a central fact table that contains quantitative measures or facts, such as sales amount, order quantity, etc., and several dimension tables that are directly connected to the fact table. A snowflake schema differs from a star schema in that the dimension tables are further split into sub-dimension tables, creating a snowflake-like shape13 A snowflake schema has some advantages and disadvantages over a star schema. Some advantages are:
It reduces the storage space required for the dimension tables, as it eliminates the redundant data.
It improves the data quality and consistency, as it avoids the update anomalies that may occur in denormalized tables.
It allows more detailed analysis and queries, as it provides more levels of dimensions.
Some disadvantages are:
It increases the complexity and number of joins required to retrieve the data from multiple tables, which may affect the query performance and speed.
It reduces the readability and simplicity of the schema, as it has more tables and relationships to understand.
It may require more maintenance and administration, as it has more tables to manage and update13