CompTIA DA0-002 Exam With Confidence Using Practice Dumps

This question falls under the Data Analysis domain, focusing on statistical methods for forecasting. The task is to anticipate (predict) the number of daily calls, which involves looking into the future.

Predictive (Option A): Predictive analytics uses historical data to forecast future outcomes (e.g., number of calls), which matches the requirement.

Diagnostic (Option B): Diagnostic analytics identifies causes and patterns in historical data, not future predictions.

Inferential (Option C): Inferential statistics make generalizations about a population, not specific forecasts.

Descriptive (Option D): Descriptive analytics summarizes past data, not suitable for anticipating future values.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and predictive analytics is the best method for forecasting future call volumes.

This question pertains to the Data Analysis domain, focusing on the purpose of a pivot table. Pivot tables are a tool for summarizing and analyzing data, often used in preparation for reporting.

To visualize the data in a dashboard (Option A): Pivot tables summarize data but aren’t visualizations; charts in dashboards might be created from pivot tables.

To retrieve and clean data from several sources (Option B): Retrieving and cleaning data is part of data preparation, not the purpose of a pivot table.

To summarize and analyze the data (Option C): Pivot tables aggregate and summarize data (e.g., by calculating sums, averages) and allow for analysis (e.g., filtering, grouping), which is their primary purpose.

To organize the data for reporting (Option D): While pivot tables can help organize data, their main purpose is summarization and analysis, not just organization.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and pivot tables are a key tool for summarizing and analyzing large datasets.

This question falls under the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores data in its original, unaltered form.

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

Data warehouse (Option B): A data warehouse stores processed, structured data, often transformed for analysis, not unaltered.

Data table (Option C): A data table is a structure within a database, not a repository, and may contain altered data.

Data factory (Option D): A data factory (e.g., Azure Data Factory) is a data integration service, not a repository for storing data.

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