CompTIA Updated DA0-002 Exam Questions and Answers by frances

This question pertains to the Visualization and Reporting domain, focusing on survey design considerations, particularly for accessibility. The survey is for Paralympic athletes, who may have visual impairments, requiring specific design considerations.

Idioms (Option A): Idioms (e.g., colloquial phrases) might confuse non-native speakers, but they’re not a primary survey design concern for Paralympic athletes.

Color contrast (Option B): High color contrast ensures readability for athletes with visual impairments (e.g., color blindness), a critical accessibility consideration for Paralympic surveys.

Refresh speed (Option C): Refresh speed is relevant for dashboards, not static surveys.

Granularity (Option D): Granularity refers to data detail levels, not a survey design consideration.

The DA0-002 Visualization and Reporting domain includes "translating business requirements to form the appropriate visualization," and color contrast is a key accessibility factor in survey design for diverse audiences.

This question falls under the Data Analysis domain, focusing on analytical methods for dashboards. The analyst wants to identify patterns in historical data for further study, which points to a specific type of analytics.

Predictive (Option A): Predictive analytics forecasts future outcomes, not focused on identifying patterns for further study.

Prescriptive (Option B): Prescriptive analytics provides recommendations, which goes beyond identifying patterns.

Diagnostic (Option C): Diagnostic analytics examines historical data to identify patterns, trends, and correlations, enabling further investigation, which fits the scenario.

Descriptive (Option D): Descriptive analytics summarizes what happened but doesn’t focus on identifying patterns for deeper study.

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

This question falls under the Data Analysis domain, focusing on selecting the appropriate function type to filter data in a query. The task is to flag customers who made a purchase in January, which involves a conditional check.

Statistical (Option A): Statistical functions (e.g., AVG, STDEV) analyze data distributions, not suitable for flagging specific months.

Logical (Option B): Logical functions (e.g., WHERE Month = 'January' in SQL) are used to apply conditions and flag rows based on criteria, which fits the task.

Mathematical (Option C): Mathematical functions (e.g., SUM, ROUND) perform calculations, not conditional flagging.

Date (Option D): Date functions (e.g., MONTH()) manipulate dates, but the Month column is already in text format, so a logical comparison is sufficient.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and logical functions are best for conditional flagging.

This question is part of the Data Concepts and Environments domain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.

Redundancy (Option A): The TypeID column (e.g., "Full Time 1") redundantly includes the Type value ("Full Time") with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.

Outlier (Option B): Outliers are data points that deviate significantly, which isn’t applicable here.

Missing data (Option C): There are no missing values in the table.

Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and redundancy is a common data quality issue in schema design.