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

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 pertains to theData Governancedomain, focusing on ensuring data quality and reliability in dashboards over time. Daily troubleshooting indicates a recurring issue, and the task is to validate data moving forward.

Inquiring about structure changes (Option A): This might identify past issues but doesn’t provide ongoing validation.

Setting up monitoring alerts (Option B): Monitoring alerts can automatically notify the analyst of data issues (e.g., missing updates, errors), providing a proactive way to validate data continuously.

Reaching out to users daily (Option C): This is inefficient and reactive, not a sustainable validation method.

Rebuilding the dashboard (Option D): Rebuilding might fix current issues but doesn’t ensure future validation.

The DA0-002 Data Governance domain includes "data quality control concepts," such as implementing monitoring to ensure data reliability in dashboards.

This question falls under theData Concepts and Environmentsdomain, focusing on selecting appropriate data types for storing specific kinds of data. The task requires storing foreign words, which often include non-native characters (e.g., accents, Unicode characters).

VARCHAR (Option A): VARCHAR stores variable-length strings but typically uses ASCII or single-byte encoding, which may not support all foreign characters.

NVARCHAR (Option B): NVARCHAR (National VARCHAR) stores variable-length strings in Unicode, supporting a wide range of non-native characters, making it the best choice.

CLOB (Option C): CLOB (Character Large Object) is for large text data, but it’s overkill for most foreign words and not specifically designed for Unicode.

CHAR (Option D): CHAR stores fixed-length strings, but like VARCHAR, it often uses single-byte encoding, which may not support foreign characters.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like NVARCHAR for Unicode support.

This question falls under theVisualization and Reportingdomain, focusing on report delivery methods. The task requires a report to be emailed every Monday for six months, indicating a scheduled, repeating process.

Building self-service access (Option A): Self-service allows users to generate reports on-demand, but the manager wants automatic delivery.

Creating a data snapshot (Option B): A snapshot captures data at a specific point, not suitable for recurring delivery over six months.

Developing a recurring process (Option C): A recurring process schedules the report to be generated and emailed every Monday, meeting the requirement for automated delivery over six months.

Waiting for the request each week (Option D): This is manual and inefficient, not suitable for a six-month schedule.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and a recurring process is ideal for scheduled email delivery.

This question falls under theData Concepts and Environmentsdomain, focusing on understanding table types in data warehousing. The task is to identify a table that holds relational keys and numeric values, typically used in a star schema.

Fact (Option A): Fact tables in a star schema store quantitative data (numeric values, e.g., sales amounts) and foreign keys (relational keys) linking to dimension tables, making this the correct choice.

Graph (Option B): Graph tables are used in graph databases for relationships (e.g., nodes, edges), not typically for relational keys and numeric values in a traditional sense.

Dimensional (Option C): Dimension tables store descriptive attributes (e.g., product names) and primary keys, not typically numeric measures.

Transactional (Option D): Transactional tables are used in OLTP systems and may contain numeric values, but they’re not specifically designed for relational keys in a data warehousing context.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and fact tables are designed to hold relational keys and numeric values in a data warehouse.

This question pertains to theData Governancedomain, focusing on data classification for security purposes. User information databases typically contain personal data, and security triggers (e.g., alerts for unauthorized access) require classifying data to determine protection levels.

Public (Option A): Public data is openly accessible (e.g., company brochures), not suitable for user information requiring security triggers.

Open (Option B): Open isn’t a standard data classification; it’s similar to public and not applicable here.

Sensitive (Option C): Sensitive data includes information that, if exposed, could cause harm (e.g., user emails, roles), which fits user information and warrants security triggers.

Non-Sensitive (Option D): Non-sensitive data doesn’t require protection, so it wouldn’t need security triggers.

Private (Option E): Private data includes PII (e.g., names, addresses) in user information databases, requiring security measures like triggers to protect against breaches.

Encrypted (Option F): Encrypted refers to a data state, not a classification; data can be classified as private or sensitive and then encrypted.

The DA0-002 Data Governance domain includes "data quality control concepts," such as classifying data to apply appropriate security measures. Sensitive and private classifications are most relevant for user information.

This question is part of theVisualization and Reportingdomain, focusing on selecting the appropriate visualization for a given dataset. The grocery store wants to view revenue by department, which requires a chart that shows proportions or comparisons across categories.

Gantt (Option A): Gantt charts are used for project scheduling, not for comparing revenue across categories.

Pie (Option B): Pie charts are ideal for showing proportions or percentages of a whole, such as revenue distribution across departments, making this the best choice.

Area (Option C): Area charts are better for showing trends over time, not static categorical comparisons.

Line (Option D): Line charts are used for trends over time, not for comparing discrete categories like departments.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization" , and a pie chart is the most appropriate for showing departmental revenue proportions.

This question is part of theData Acquisition and Preparationdomain, which involves combining data from multiple sources. The files have the same structure but different values, meaning theyneed to be stacked vertically into one dataset.

Merging (Option A): Merging typically involves joining datasets on a common key (e.g., a customer ID), which isn’t indicated here since the files only differ in values, not keys.

Appending (Option B): Appending stacks datasets vertically, combining rows from files with the same structure into a single dataset, which matches the scenario.

Parsing (Option C): Parsing involves breaking down data (e.g., splitting text), not combining files.

Clustering (Option D): Clustering is a machine learning technique for grouping similar data points, not for combining files.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as appending datasets with identical structures.

This question pertains to theVisualization and Reportingdomain, focusing on selecting the appropriate visualization for a specific requirement. The task is to show the composition of total sales across three channels, which involves showing proportions.

Pivot table (Option A): A pivot table summarizes data but isn’t a visualization; it’s more for data exploration.

Pie chart (Option B): A pie chart shows the proportion of total sales for each channel (Digital, Store, Online), which is ideal for displaying composition.

KPI card (Option C): A KPI card displays a single metric, not suitable for showing composition across multiple channels.

Box plot (Option D): A box plot shows data distribution (e.g., quartiles), not proportions.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a pie chart is best for showing the composition of totals.

This question falls under theData Governancedomain of CompTIA Data+ DA0-002, which includes understanding compliance frameworks for data audits, especially for customer data. The scenario involves a data audit of customer invoice data, which likely contains personal information, making privacy regulations relevant.

PCI DSS (Option A): PCI DSS (Payment Card Industry Data Security Standard) applies specifically to payment card data, not general customer invoice data unless credit card details are involved, which isn’t specified.

GDPR (Option B): GDPR (General Data Protection Regulation) is a comprehensive privacy regulation for handling personal data of EU citizens, including customer invoice data, which may contain PII like names and addresses. It’s the most relevant for a company-wide data audit.

ISO (Option C): ISO standards (e.g., ISO 27001) relate to information security management but are not specific to customer data privacy audits.

PII (Option D): PII (Personally Identifiable Information) is a concept, not a framework or tool for conducting an audit.

The DA0-002 Data Governance domain emphasizes "identifying PII and data privacy concepts," and GDPR is the most appropriate framework for auditing customer data to ensure compliance with privacy laws.