Free Practice Questions for CompTIA DA0-001 Exam

Create a dashboard with a date range picker and calculations built in. This is because a dashboard is a type of visualization that displays multiple charts or graphs on a single page, usually to provide an overview or summary of some data or information. A dashboard can be used to track company sales across various date ranges by showing different metrics and indicators related to sales, such as revenue, volume, or growth. By creating a dashboard with a date range picker and calculations built in, the analyst can suggest a way for the user to have a dynamic, seamless experience, which means that the user can interact with and customize the dashboard according to their needs or preferences, as well as avoid any manual work or errors. For example, a date range picker is a type of feature or function that allows users to select or adjust the time period for which they want to see the data on the dashboard, such as daily, weekly, monthly, or quarterly. A date range picker can make the dashboard dynamic, as it can automatically update or refresh the dashboard with new data based on the selected time period. Calculations are mathematical operations or expressions that can be performed on the data on the dashboard, such as addition, subtraction, multiplication, division, average, sum, etc. Calculations can make the dashboard seamless, as they can eliminate the need for manual calculations for each date range, as well as ensure accuracy and consistency of the results. The other ways are not the best ways to provide a dynamic, seamless experience for the user. Here is why:

Creating multiple reports, one for each needed date range would not provide a dynamic, seamless experience for the user, but rather create a static, cumbersome experience, which means that the user cannot interact with or customize the reports according to their needs or preferences, as well as have to deal with multiple files or pages. For example, creatingmultiple reports would make it difficult for the user to compare or contrast the sales across different date ranges, as well as increase the workload and complexity of managing and maintaining the reports.

Building calculations into the report so they are done automatically would not provide a dynamic, seamless experience for the user, but rather provide a partial, limited experience, which means that the user can only benefit from one aspect or feature of the report, but not from others. For example, building calculations into the report would help with avoiding manual work or errors, but it would not help with interacting with or customizing the report according to different date ranges.

Adding macros to the report to speed up the filtering and calculations process would not provide a dynamic, seamless experience for the user, but rather provide an advanced, complex experience, which means that the user would need to have some technical skills or knowledge to use or apply the macros, as well as face some potential risks or challenges. For example, adding macros to the report would require the user to know how to write or run the macros, which are a type of code or script that automates certain tasks or actions on the report, such as filtering or calculating the data. Adding macros to the report could also expose the user to some security or compatibility issues, such as viruses, malware, or errors.

The best deliverable that would suit the site reliability team’s needs is A. A self-serve dashboard of website performance that updates in real time.

A self-serve 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 glance. A self-serve dashboard of website performance that updates in real time would allow the site reliability team to easily and quickly access the information they need about the stability of their website, such as uptime, response time, error rate, traffic volume, etc. A self-serve dashboard would also enable the team to proactively diagnose issues when they occur, by providing alerts, notifications, or drill-down options. A self-serve dashboard would also be more interactive and engaging than a report or an email.

A weekly log report of site visits and user actions would not be a good deliverable for the site reliability team’s needs, because it would not provide timely or relevant information about the stability of their website. A weekly log report would be too infrequent and delayed to monitor and diagnose issues when they occur. A weekly log report would also focus on the behavior and actions of the users, rather than the performance and functionality of the website.

A portal that is refreshed daily and reports errors classified by type would not be a good deliverable for the site reliability team’s needs, because it would not provide real-time or comprehensive information about the stability of their website. A portal that is refreshed daily would be too slow and outdated to monitor and diagnose issues when they occur. A portal that reports errors classified by type would be too narrow and limited to capture the full picture of the website performance.

A daily summary email indicating website outages for the previous day would not be a good deliverable for the site reliability team’s needs, because it would not provide real-time or actionable information about the stability of their website. A daily summary email would be too late and retrospective to monitor and diagnose issues when they occur. A daily summary email indicating website outages would also be too passive and generic to help the team resolve or prevent issues in the future.

VARCHAR is a data type optimized for storing variable-length strings. It only uses the necessary space for each value, making it more efficient in terms of storage compared to CHAR, which allocates a fixed amount of space regardless of the string's actual length​.

Neo4j is a type of non-relational database that uses a graph model to store data. A graph database is a database that represents data as nodes and edges, where nodes are entities and edges are relationships between them. A graph database can store complex and diverse data that is not easily structured in tables. A graph database can also perform fast and efficient queries on the data by traversing the connections between the nodes

Two reasons to conduct data cleansing are:

To improve accuracy: Data cleansing helps to ensure that the data is correct, consistent, and reliable.This can improve the quality and validity of the analysis, as well as the decision-making and outcomes based on the data12

To calculate trends: Data cleansing helps to remove or resolve any errors, outliers, or missing values that could distort or skew the data.This can help to identify and measure the patterns, changes, or relationships in the data over time13

Data profiling is the process of examining data for statistics and information about the data, such as the structure, format, quality, and content of the data. Data profiling can help to understand the characteristics, patterns, relationships, and anomalies of the data, as well as to identify and resolve any errors, inconsistencies, or missing values in the data. Data profiling can be done using various tools and methods, such as spreadsheets, databases, or programming languages12.

Outliers are data points that differ significantly from other observations and can skew analysis results, leading to inaccurate conclusions. The main reason to remove them is when their values vary so significantly from the rest of the data that they distort statistical calculations or trends.

CompTIA Data+ Reference:

CompTIA Data+ Study Guide (Exam DA0-001), Chapter 5: Data Cleaning and Preparation, Section “Identifying and Handling Outliers”, Official CompTIA CertMaster Learn for Data+, Module 5.1 “Data Quality Issues: Outliers”.

The first step the analyst should perform with the data is to conduct an exploratory analysis and use descriptive statistics. Exploratory analysis is a type of analysis that aims to summarize the main characteristics of the data, identify patterns, outliers, and relationships, and generate hypotheses for further investigation. Descriptive statistics are numerical measures that describe the central tendency, variability, and distribution of the data, such as mean, median, mode, standard deviation, range, quartiles, etc. Exploratory analysis and descriptive statistics can help the analyst gain a betterunderstanding of the data and its quality, as well as prepare the data for further analysis.

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.