Pass the CompTIA Data+ DA0-002 Questions and answers with ExamsMirror

This is a simulation question that requires you to create a dashboard with visualizations that meet the director’s needs.Here are the steps to complete the task:

Drag and drop the visualization that shows the county outages on the top left space of the dashboard. This visualization is a map of the state with different colors indicating the number of outages in each county. You can choose any color scheme that suits your preference, but make sure that the colors are consistent and clear. For example, you can use a gradient of red to show the counties with more outages and green to show the counties with less outages.

Drag and drop the visualization that shows the status of the outages on the top right space of the dashboard. This visualization is a pie chart that shows the percentage of outages that are active, restored, or pending. You can choose any color scheme that suits your preference, but make sure that the colors are distinct and easy to identify. For example, you can use red for active, green for restored, and yellow for pending.

Drag and drop the visualization that shows the overall trend of outages on the bottom space of the dashboard. This visualization is a line graph that shows the number of outages over time. You can choose any color scheme that suits your preference, but make sure that the color is visible and contrasted with the background. For example, you can use blue for the line and white for the background.

Select appropriate titles and labels for each visualization. Titles and labels may be used more than once. For example, you can use “County Outages” as the title for the map, “Status” as the title for the pie chart, and “Trend” as the title for the line graph. You can also use “County”, “Number of Outages”, “Active”, “Restored”, “Pending”, “Time”, and “Number of Outages” as labels for the axes and legends of the visualizations.

This question falls under theData Acquisition and Preparationdomain, focusing on modifying historical data. The task is to add new classification codes to existing data, which involves adding new rows or columns.

Append (Option A): Appending adds new rows to a dataset, which is suitable if the classification codes are new records (e.g., a new table of codes to combine with historical data). If the codes are a new column, a join or update might be used, but append fits the context of adding new data.

Binning (Option B): Binning groups data into categories, not suitable for adding classification codes.

Parsing (Option C): Parsing breaks down data (e.g., splitting strings), not relevant for adding codes.

Union (Option D): Union stacks tables with identical structures, but the task involves adding new data (codes) to historical data, not combining identical tables.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and appending is a common technique for adding new data to historical datasets.

This question is part of theVisualization and Reportingdomain, focusing on report delivery methods. The requirement for a report that can be generated and accessed at any time suggests user-driven access.

Ad hoc (Option A): Ad hoc reports are one-time, on-demand reports, not designed for anytime access by the user.

Dynamic (Option B): Dynamic reports allow interactivity but don’t necessarily imply user-generated access at any time.

Self-service (Option C): Self-service reporting allows users (e.g., the senior manager) to generate and access reports on-demand at any time, fitting the requirement.

Static (Option D): Static reports are fixed and don’t allow on-demand generation by the user.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and self-service is ideal for anytime access.

This question falls under theData Analysisdomain, focusing on statistical measures. The mode is the value that appears most frequently in a dataset.

Monday: 455

Tuesday: 346

Wednesday: 382

Thursday: 563

Friday: 887

Saturday: 934

Sunday: 346

The value 346 appears twice (Tuesday and Sunday), while all other values (455, 382, 563, 887, 934) appear once. Thus, the mode is 346.

Option A: 346– Correct, as it’s the most frequent value.

Option B: 446– Incorrect, as 446 doesn’t appear in the dataset.

Option C: 455– Incorrect, as 455 appears only once.

Option D: 559– Incorrect, as 559 doesn’t appear in the dataset.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the mode is a fundamental measure of central tendency.

This question falls under theVisualization and Reportingdomain of CompTIA Data+ DA0-002, focusing on selecting the appropriate visualization for a specific dataset. The task is to show availability zones over a 15-year period (past and future), which involves a geographical element since availability zones are typically location-based.

Bar chart (Option A): Bar charts are good for comparing categorical data but don’t effectively show geographical locations or time-based trends across zones.

Mosaic plot (Option B): Mosaic plots display relationships between categorical variables, not suitable for geographical or time-series data.

Map (Option C): A map can display availability zones geographically, with annotations or layers to show changes over time (past 10 years and future 5 years), making it the most appropriate visualization.

Pie chart (Option D): Pie charts show proportions of a whole, not suitable for geographical or time-based data.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a map is the best choice for displaying geographical availability zones over time.

This question falls under theData Analysisdomain, focusing on analytical methods for reporting. The task is to identify the causes of customer churn over three years, which involves analyzing historical data.

Inferential (Option A): Inferential statistics make predictions or generalizations about a population, not focused on identifying causes in historical data.

Descriptive (Option B): Descriptive analytics summarizes historical data to identify patterns and causes (e.g., reasons for churn), which fits the task.

Prescriptive (Option C): Prescriptive analytics provides recommendations, which goes beyond identifying causes.

Predictive (Option D): Predictive analytics forecasts future outcomes, not focused on historical causes.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and descriptive analytics is best for identifying causes in historical data.

This question is related to theData Governancedomain of DA0-002, which includes understanding processes like User Acceptance Testing (UAT) to ensure data-related applications meet governance and quality standards. UAT is a critical step in ensuring software aligns with user needs and organizational requirements.

To begin the software application development process to enhance user experience (Option A): UAT occurs near the end of development, not at the beginning.

To ensure all parts of the software application work together after each sprint (Option B): This describes integration testing, not UAT, which focuses on user validation.

To review software application crashes, create patches, and deploy to users (Option C): This refers to post-deployment maintenance, not UAT.

To validate and verify that a software application meets the needs and requirements of users (Option D): UAT is specifically designed to ensure the software meets user requirements and functions as intended in a real-world scenario, aligning with governance standards for quality.

The DA0-002 Data Governance domain emphasizes "data quality control concepts" (similar to DA0-001, web ID: 1), which include ensuring applications meet user needs through processes like UAT.

This question falls under theData Governancedomain, focusing on access control practices for data security. The task is to limit an employee’s access to a production environment, requiring a structured approach.

Mandatory (Option A): Mandatory access control (MAC) uses strict, system-enforced rules (e.g., military settings), but it’s overly rigid for most companies.

Time-based (Option B): Time-based access limits access to specific times, which doesn’t address general production environment access.

Attribute-based (Option C): Attribute-based access control (ABAC) uses attributes (e.g., department, location), but it’s complex and not the simplest solution.

Role-based (Option D): Role-based access control (RBAC) assigns permissions based on the employee’s role, ensuring they only access what’s needed for their job, making it the best practice for limiting production access.

The DA0-002 Data Governance domain includes "data privacy concepts," and role-based access control is a widely adopted practice for limiting access in production environments.

This question falls under theVisualization and Reportingdomain of CompTIA Data+ DA0-002, which involves understanding the components necessary for designing a report. The given requirements cover data, visuals, and design, but a key aspect of report planning is missing.

Source validation (Option A): Source validation ensures data accuracy, but it’s typically part of the data preparation phase, not a report design requirement.

Design elements (Option B): Color branding, labels, and terminology are design elements, so this is already included.

Delivery method (Option C): The delivery method (e.g., recurring, ad hoc, self-service) specifies how the report will be distributed or accessed, which is a critical requirement missing from the list.

Report type (Option D): Suggested charts and tables imply the report type (e.g., summary, dashboard), so this is indirectly covered.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and the delivery method is a key component of report planning that’s missing here.

Foundational models are large AI models trained on vast amounts of data, often exhibiting strong generalization capabilities. While not specifically architected for time-series, their ability to learn complex patterns could potentially be leveraged for forecasting tasks through fine-tuning or specialized architectures built upon them.

In reality, the best AI types specifically designed for time-series forecasting include:

Recurrent Neural Networks (RNNs), especially LSTMs and GRUs:These architectures are designed to handle sequential data and capture temporal dependencies.

Transformer Networks:Originally developed for NLP, Transformers have shown remarkable success in time-series forecasting due to their ability to capture long-range dependencies.

Traditional statistical models:ARIMA, Exponential Smoothing, and other statistical methods remain powerful and interpretable options for time-series analysis.

Therefore, while "foundational models" have some potential, it's important to understand that they aren't the primary or specifically designed AI type for time-series forecasting.