Free Practice Questions for CompTIA DA0-002 Exam

This question falls under the Data Analysis domain, focusing on SQL logical functions for query evaluation. The task is to evaluate "all conditions," implying multiple conditions must be true together.

OR (Option A): OR returns true if any condition is true, not ensuring all conditions are met.

NOT (Option B): NOT negates a condition, not suitable for combining multiple conditions.

AND (Option C): AND requires all conditions to be true, which aligns with evaluating "all conditions" in a query.

IF (Option D): IF is a conditional function for decision-making, not for evaluating multiple conditions together.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and AND is the best logical function for ensuring all conditions are met.

This question pertains to the Visualization and Reporting domain, focusing on dashboard design for specific audiences. Internal technical audiences (e.g., data analysts, IT staff) need actionable, data-driven insights.

Methodology section (Option A): Methodology is important for research reports, not dashboards, especially for technical audiences who prioritize data.

Dynamic features (Option B): Dynamic features (e.g., interactivity) are useful but not the most critical element for technical audiences.

Key performance indicators (Option C): KPIs provide critical metrics (e.g., system uptime, error rates) that technical audiences need to monitor and act on, making this the most important element.

Company branding (Option D): Branding is more relevant for external audiences, not internal technical ones.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and KPIs are essential for technical dashboards.

This question pertains to the Data Acquisition and Preparation domain, which in DA0-002 includes understanding data acquisition concepts and the types of data gathered from various sources, such as web scraping. Web scraping involves extracting data from websites, and the data gathered is often semi-structured, meaning it has some organizational structure but isn’t fully relational like a database table.

JSON (Option A): JSON (JavaScript Object Notation) is a semi-structured data format commonly used in web applications. Web scraping often retrieves data in JSON format via APIs or embedded scripts, as it’s lightweight and structured with key-value pairs, making it ideal for semi-structured data.

CSV (Option B): CSV (Comma-Separated Values) is a structured format typically used for tabular data. It’s not commonly the direct output of web scraping, though scraped data might be converted to CSV later.

CSS (Option C): CSS (Cascading Style Sheets) is used for styling web pages and isn’t a data format, making it irrelevant for describing scraped data.

HTML (Option D): HTML (HyperText Markup Language) is the structure of web pages and is often the raw format scraped during web scraping. While HTML is semi-structured due to its tag-based hierarchy, it’s primarily a markup language, not a data format, and the actual data extracted is often parsed into formats like JSON.

The DA0-002 Data Acquisition and Preparation domain aligns with the DA0-001 focus on "data acquisition concepts" (web ID: 14), which includes identifying formats like JSON as semi-structured data commonly acquired through web scraping. JSON is the best fit here due to its prevalence in web data exchange.

This question falls under the Data Concepts and Environments domain, which in DA0-002 involves understanding database environments, connections, and troubleshooting issues related to data access. The scenario describes a report failing after a database upgrade, indicating a potential issue with the database environment or connectivity.

Ensure the system has permissions for the report service (Option A): While permissions issues can cause report failures, they are less likely to be the first issue after a database upgrade unless explicitly mentioned.

Change the report's refresh rate (Option B): Refresh rate adjustments might address performance issues but won’t resolve a fundamental error preventing data display.

Verify the connection to the database (Option C): A database upgrade often involves changes to connection strings, drivers, or network configurations. Verifying the connection ensures the report can access the database, making this the most logical first step.

Check whether the data structures were modified (Option D): While possible, checking data structures (e.g., schema changes) is a deeper troubleshooting step that should follow after confirming basic connectivity.

The DA0-002 Data Concepts and Environments domain includes understanding database connectivity, similar to DA0-001’s focus on "data schemas and dimensions" and environments (web ID: 1). Verifying the connection is the first recommended step in troubleshooting post-upgrade issues.

This question pertains to the Data Concepts and Environments domain, focusing on file formats for data sharing. The task is to identify the best reason to choose CSV over Excel for sharing data.

CSV files can store different types of encoding (Option A): While CSV files can use different encodings, this isn’t the primary reason to choose them over Excel.

CSV files are not vendor-specific (Option B): CSV is a plain-text format that can be opened by any software, unlike Excel files, which are tied to Microsoft Excel, making CSV more interoperable and the best reason for sharing.

CSV files are smaller in size (Option C): CSV files are often smaller due to their simplicity, but this isn’t always the primary reason for sharing.

CSV files are easier to change in text editors (Option D): While true, this isn’t the most compelling reason for sharing data across systems.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and CSV’s vendor-neutral nature makes it ideal for sharing data.

This question falls under the Data Concepts and Environments domain, 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 pertains to the Visualization and Reporting domain, focusing on report delivery frequencies. The report is described as unscheduled, meaning it’s a one-time request.

Annual (Option A): Annual frequency implies a scheduled report every year, not suitable for an unscheduled request.

Daily (Option B): Daily frequency implies a scheduled report each day, not suitable.

Weekly (Option C): Weekly frequency implies a scheduled report each week, not suitable.

Ad hoc (Option D): Ad hoc reports are generated on-demand for one-time or unscheduled needs, which matches the project manager’s request.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and ad hoc is the best frequency for an unscheduled report.

This question falls under the Data Governance domain, focusing on optimizing data quality and performance in dashboards. The dashboard is slow because it pulls from a large database (30 years) but only needs seven years of data.

Performing a code review (Option A): A code review might identify inefficiencies, but it’s not the most direct solution for this scenario.

Checking network connectivity (Option B): Network issues might cause delays, but the primary issue is the data volume, not connectivity.

Filtering to include only relevant data (Option C): Filtering the data to include only the last seven years reduces the dataset size, directly improving performance by minimizing the data processed.

Adding more RAM and rerunning (Option D): Adding RAM might help, but it’s a hardware solution that doesn’t address the root cause of excessive data.

The DA0-002 Data Governance domain includes "data quality control concepts," such as optimizing performance by filtering data to improve efficiency.

This question pertains to the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores raw, unformatted data.

Data warehouse (Option A): A data warehouse stores structured, processed data in a predefined schema, not raw data.

Data silo (Option B): A data silo is an isolated repository, often structured, not designed for raw data storage.

Data mart (Option C): A data mart is a subset of a data warehouse, also storing structured data.

Data lake (Option D): A data lake stores raw, unformatted data in its original format (structured, semi-structured, or unstructured), making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed for raw data storage.