CompTIA Data+ DA0-002 (V2) Exam Practice Test 5

KPIs are the specific, pre-agreed metrics identified as the most important indicators of business health, exactly matching these three leadership-agreed operational metrics. A mock-up is an early draft prototype, user persona type concerns role-based content tailoring, accessibility concerns usability for people with disabilities, and level of detail concerns content granularity rather than describing these specific named business metrics.

A legend explains what different colors or line styles represent across multiple data series within a visual, exactly matching this revenue-versus-costs key. Labels identify specific data point values, branding applies company visual identity, color schemes generically concern color selection and consistency, and infographics are a visual type rather than this symbol-explanation design element.

Labels identify specific data points, such as the exact numeric value next to each bar, helping viewers avoid estimating from the scale alone. Legends explain what colors or symbols represent across multiple series, branding applies company visual identity, color schemes concern color selection, and infographics are a visual type rather than this specific value-labeling design element.

A misspelled table name causing a syntax error is a basic SQL code issue. Connectivity-related issues concern failed database connections, user-reported generically describes how an issue was flagged rather than its cause, and corrupted data and stale data concern the state of the data itself rather than a query syntax error.

Data profiling systematically analyzes a dataset's columns, calculating statistics like null percentage, value ranges, and category distribution, to understand overall quality characteristics. A unit test checks individual code components, source control manages code versioning, a user acceptance test has business users validate real-world fitness, and a stress test evaluates performance under heavy load rather than analyzing data content characteristics.

Jurisdictional requirements concern complying with the specific local laws of the country where data is collected, which may differ from the company's home country regulations. Retention concerns how long data is kept, replication concerns copying data across locations, audit generically describes broader compliance review activity, and classification categorizes data by sensitivity rather than addressing country-specific legal variation.

A hierarchy structure documents how data elements roll up into broader groupings, exactly matching SKUs rolling up into subcategories and departments.

Ad hoc frequency describes reports generated on demand as specific questions arise, outside of any regular scheduled cadence. Recurring frequency delivers on a fixed schedule, real-time and snapshot describe data versioning rather than delivery scheduling, and static describes a fixed, non-interactive dashboard view rather than this on-demand request pattern.

Real-time data versioning continuously reflects the current moment as new data arrives, rather than a saved point-in-time copy. A snapshot captures data at a specific point in time, recurring and ad hoc frequency describe delivery scheduling rather than continuous live data, and a static dashboard shows a fixed view rather than continuously updating data.

Code review examines the report-generating code itself, calc review specifically examines calculation and formula logic, and peer review has a colleague independently check the work more generally, each representing a distinct validation review type.

Formatting errors introduced by a manual edit that cause fields to be misread is a corrupted data issue. Connectivity-related issues concern failed connections, user-reported generically describes how an issue was flagged rather than its cause, basic SQL code issues concern query syntax errors, and stale data concerns outdated but otherwise correctly formatted figures rather than genuinely damaged formatting.

String functions manipulate text values, such as extracting the first three characters of a product code, exactly matching this text-based extraction. Mathematical functions perform numeric calculations, logical functions evaluate true/false conditions, date functions manipulate date and time values, and measures of central tendency describe where numeric data clusters rather than text character extraction.

Predictive statistics use historical data and modeling to forecast future outcomes, exactly matching estimating the probability of a future purchase based on past patterns. Descriptive statistics summarize what has already happened, prescriptive methods recommend specific actions, inferential methods draw conclusions about a population from a sample, and diagnostic is not one of the four basic statistical method categories in this objective.

A bridge table resolves a many-to-many relationship between two entities by linking paired ID references, exactly matching this student-course enrollment scenario. A fact table stores numeric measures, a dimension table stores descriptive attributes, a schema describes overall database structure, and a nested structure describes hierarchical data within semi-structured formats rather than resolving many-to-many relationships.

Ensuring content is readable by screen readers and includes descriptive alt text is the accessibility consideration, supporting users with visual impairments. Level of detail concerns content granularity, sensitive versus non-sensitive concerns confidentiality, technical versus non-technical audience concerns language complexity, and internal versus external concerns organizational boundary rather than usability for people with disabilities.

Adding new rows to an existing table without altering existing rows is appending, while extracting a specific piece of structured information, like an area code, from unstructured text is parsing. Merging combines multiple fields or datasets together, augmentation adds new external data to enrich a dataset, and deletion removes records or fields entirely rather than describing either of these two described techniques.

Scaling adjusts the range of numeric values, such as normalizing to a 0-1 range, so columns with different original ranges can be fairly compared. Binning groups continuous values into discrete categories rather than rescaling the numeric range itself, imputation fills in missing values, exploding expands array fields into multiple rows, and parsing extracts structured information from unstructured text.

The same physical product represented under two different codes due to differing numbering schemes is duplication, since the same real-world entity appears as separate records. Missing values describes absent data, outliers describes unusual extreme values, completeness describes whether all expected data is present, and validation/formatting inconsistency describes inconsistent representations of the same value within one system rather than genuinely separate duplicate entity records across systems.

Completeness concerns whether all expected data is present, including entire fields being absent from one source prior to a merge. Duplication describes repeated entity records, redundancy describes unnecessarily repeated data, outliers describes unusual extreme values, and validation describes checking data against defined rules and formats rather than the presence or absence of an entire field.

Surveying gathers structured feedback by directly asking a standardized set of questions, exactly matching this customer satisfaction data collection. Sampling selects a representative subset of a population rather than describing the question-asking method itself, web scraping extracts data from websites, data integration combines data from multiple sources, and auditing reviews compliance or accuracy rather than describing direct question-based data collection.

A filter restricts which rows are returned based on a specified condition, exactly matching limiting results to orders from the last 30 days. A join combines columns from related tables, a union stacks rows from multiple queries, aggregate summarizes multiple rows into a single value, and concatenate combines text strings together rather than restricting which rows appear in a result set.

Robotic process automation executes rule-based backend steps, such as updating a ticket status, without human involvement. Natural language processing handles the language understanding portion of the chatbot rather than the backend automation, foundational models generically and deep learning generically describe underlying AI model types, and generative AI specifically creates new content rather than executing rule-based backend steps.

pandas is a widely used Python library specifically designed for data manipulation and analysis through its DataFrame structure, distinct from tidyverse, the equivalent collection of packages in R.

Block storage provides raw, low-level volumes that can be formatted with any file system and mounted directly, offering fast, fine-grained access similar to a physical drive. Object storage stores files with metadata via API rather than raw formattable volumes, file storage provides a hierarchical directory structure, shared storage describes network accessibility broadly, and local alone describes physical attachment rather than this specific raw-volume storage type.

A data mart is a smaller, subject-specific structured subset of data for a particular business area, exactly matching this merchandising-focused inventory subset. A data lake stores raw data broadly, a data silo is isolated and inaccessible to others, a data lakehouse combines lake and warehouse capabilities, and a data warehouse alone describes the full enterprise-wide repository rather than this focused subset.