DA0-001 Exam Dumps - CompTIA Data+ Certification Exam

The correct answer is A. 13.2%.

The total order conversion rate is the ratio of the total number of orders to the total number of clicks, expressed as a percentage. To calculate the total order conversion rate, we need to sum up the clicks and orders from all the channels, and then divide the orders by the clicks and multiply by 100.

Using the data from the table, we can do the following:

Total clicks = 580 + 800 + 1,200 + 300 + 620 = 3,500

Total orders = 55 + 100 + 220 + 60 + 85 = 520

Total order conversion rate = (520 / 3,500) x 100 = 14.857%

Rounding to one decimal place, we get 14.9%

Therefore, the total order conversion rate is 14.9%.

The best format to deliver this report is D. A dashboard with filters at the top that the user can toggle.

A dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance1A dashboard with filters at the top that the user can toggle would allow the user to easily and quickly access the information they need about various regions, products, and time periods, without having to navigate through multiple tabs, pages, or emails. A dashboard with filters would also enable the user to compare and contrast different views of the data and see how they change over time.A dashboard with filters would also be more interactive and engaging than a static or email report2

A workbook with multiple tabs for each region would not be an efficient way to deliver this report, because it would require the user to switch between different tabs to see the information they need. This would make it harder to compare and contrast different regions, products, and time periods, and also increase the risk of errors or confusion.A workbook with multiple tabs would also be less visually appealing and more cluttered than a dashboard3

A daily email with snapshots of regional summaries would not be an efficient way to deliver this report, because it would limit the user’s ability to explore the data in depth and customize their view. A daily email would also be dependent on the frequency and timing of the email delivery, which might not match the user’s needs or preferences. A daily email would also be more likely to be ignored or deleted than a dashboard that is always accessible.

A static report with a different page for every filtered view would not be an efficient way to deliver this report, because it would create a very long and cumbersome report that would be difficult to read and understand. A static report would also not allow the user to change or update the filters as they wish, or see how the data changes over time. A static report would also be less interactive and engaging than a dashboard.

This is because duplicate data refers to data that is repeated or copied in a data set, which can affect the quality and validity of the analysis. A DISTINCT function is a type of function that removes duplicate values from a column or a table, leaving only unique values. For example, a DISTINCT function in SQL that can achieve this is:

The other data cleansing issues will not be fixed by applying a DISTINCT function. Here is why:

Missing data refers to data that is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis. A DISTINCT function does not help with missing data, because it does not fill in or impute the missing values.

Redundant data refers to data that is unnecessary or irrelevant for the analysis, which can affect the efficiency and performance of the analysis. A DISTINCT function does not help with redundant data, because it does not remove or filter out the redundant values.

Invalid data refers to data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis. A DISTINCT function does not help with invalid data, because it does not validate or correct the invalid values.

Data aggregation is the process of compiling data from multiple sources and summarizing it into a single dataset. Data aggregation can be used to calculate statistics, such as averages, sums, counts, or percentages. In this case, the analyst must obtain the average daily sales for the following week, which is a statistic that can be calculated by aggregating the sales data from each day and dividing by the number of days. Data aggregation can be done using various tools and methods, such as spreadsheets, databases, or programming languages.

Although the specific table isn’t shown here, this question type in CompTIA-style practice typically presents a score table where some values:

Are outside the valid range (e.g., scores > 100 on a 0–100 test, or negative scores)

Or use non-numeric or nonsensical entries for a score field (e.g., “N/A”, “ABC” where numeric values are required)

When values violate defined business rules or constraints (e.g., “exam scores must be between 0 and 100 and numeric”), they are considered invalid data.

Why the other options are not the best reason:

Redundant data (B): Would mean repeated/duplicate copies of the same information. That doesn’t address bad or impossible values.

Data outliers (C): Outliers are extreme but possible values within the defined range. Here the driver for cleansing is not just extremity but rule violation (e.g., >100 on a test that maxes at 100).

Missing data (D): Would concern null/blank fields, which is a different quality issue.

The key phrase in this type of question is that some values do not conform to the allowed or logical rules of the field. In CompTIA Data+ terms, this is a classic case of Invalid data (A) and is a primary driver for data cleansing.

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Domain: Data Quality (identifying invalid, inconsistent, incomplete data).

CompTIA Data+ Official Study Guide – section on data quality dimensions, highlighting validity as adherence to defined business rules (e.g., allowed ranges for scores).

When analyzing relationships betweentwo numeric variables, the best visualization is ascatter plot, as it helps to:

Identifycorrelations(positive, negative, or no correlation).

Detectclustering patternsin data points.

Spotoutliersthat deviate significantly from the general trend.

Option A (Heat map):Incorrect. Heat maps are used for visualizing intensity differences across categories, not for identifying patterns between two numeric variables.

Option B (Tree map):Incorrect. Tree maps visualize hierarchical relationships, not scatter patterns.

Option C (Scatter plot):Correct.Scatter plots are best suited for examining relationships and identifying clustering patterns and outliers.

Option D (Stacked chart):Incorrect. Stacked charts are used to display the composition of multiple data series, not relationships between two numeric variables.

The dataset exhibits inconsistencies in both the CustomerID and Date columns:

CustomerID Column: The identifiers use various formats, including underscores (Tre_234), slashes (215/Tre), hyphens (Tre-345), and combinations of letters and numbers (TREJD19). This inconsistency can lead to difficulties in data retrieval and analysis.

Date Column: Dates are presented in multiple formats, such as YYYY-MM-DD (2022-09-08), MM/DD/YYYY (08/02/2021), YYYY/MM/DD (2021/06/20), MM-DD-YYYY (12-26-2021), DD/MM/YYYY (31/08/2022), and an unconventional format (2022'08/01). Such variability can cause errors in date computations and analyses.

Addressing these inconsistencies is essential to ensure data quality and reliability.

Explanation

Data privacy defines who has access to data, while data protection provides tools and policies to actually restrict access to the data. Compliance regulations help ensure that user's privacy requests are carried out by companies, and companies are responsible to take measures to protect private user data.

Why is data privacy important?

When data that should be kept private gets in the wrong hands, bad things can happen. A data breach at a government agency can, for example, put top secret information in the hands of an enemy state. A breach at a corporation can put proprietary data in the hands of a competitor.