CompTIA Data+ DA0-002 Passing Score

This question falls under the Data Analysis domain, focusing on techniques for segmenting data. The task is to segment customers based on spending behavior, which involves grouping numerical data (Amount_spent) into categories.

Standardization (Option A): Standardization scales numerical data to a common range (e.g., z-scores), but it doesn’t segment customers into groups.

Concatenate (Option B): Concatenation combines text fields, not numerical data for segmentation.

Binning (Option C): Binning involves grouping numerical data into discrete intervals (e.g., low, medium, high spending), which is ideal for segmenting customers based on spending behavior.

Appending (Option D): Appending combines datasets vertically, not relevant for segmentation.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a common method for segmenting numerical data like spending amounts.

This question is related to the Data Governance domain 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 the Visualization and Reporting domain, focusing on report refresh frequencies. The manager needs the report to always be up-to-date, implying continuous data updates.

Real-time (Option A): Real-time refresh frequency ensures the report reflects the latest data as soon as it changes, which matches the requirement to "always be up-to-date."

Ad hoc (Option B): Ad hoc reports are generated on-demand, not continuously updated.

Snapshot (Option C): A snapshot captures data at a specific point in time, not suitable for always being up-to-date.

Dynamic (Option D): Dynamic reports allow interactivity, but the term doesn’t specifically imply real-time updates.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and real-time refresh frequency ensures the report is always current.

This question falls under the Data Concepts and Environments domain, which involves understanding documentation types related to data management. The document describes key metrics and their calculation standards, which points to a specific type of metadata documentation.

Data dictionary (Option A): A data dictionary defines data elements, including metrics, their meanings, and calculation methods, ensuring consistency across the organization. This matches the description.

Data explainability report (Option B): This term is more associated with AI/ML, explaining model decisions, not metric definitions.

Data lineage (Option C): Data lineage tracks the flow of data through systems, not metric definitions or calculations.

Data flow diagram (Option D): A data flow diagram visualizes data processes, not metric standards.

The DA0-002 Data Concepts and Environments domain includes understanding "basic concepts of data schemas and dimensions" , and a data dictionary is a foundational tool for defining metrics.