Data Analysis & Reporting Flashcards
6 cards from real GCP practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Data Analysis & Reporting flashcards as text
What is the purpose of data analysis in GCP practice?
Answer: To transform raw data into meaningful insights that support informed decision-making
Data analysis involves examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making processes with evidence-based insights.
What is the difference between quantitative and qualitative data in GCP analysis?
Answer: Quantitative data is numerical and measurable; qualitative data is descriptive and categorical
Quantitative data consists of numerical measurements that can be statistically analyzed, while qualitative data consists of descriptive observations, opinions, and categories that provide context and depth.
What is a dashboard in GCP data reporting?
Answer: A visual display of key metrics and data points for at-a-glance monitoring of performance
A dashboard provides a consolidated visual display of important metrics, KPIs, and data trends, enabling stakeholders to quickly assess performance status and identify areas needing attention.
What does data integrity mean in GCP practice?
Answer: The accuracy, consistency, and reliability of data throughout its entire lifecycle
Data integrity refers to maintaining and assuring the accuracy, consistency, and reliability of data over its entire lifecycle, ensuring that information remains trustworthy for analysis and decision-making.
What is trend analysis in GCP reporting?
Answer: Examining data over time to identify patterns, directions, and changes in performance
Trend analysis examines historical data across multiple time periods to identify patterns, direction of change, and rate of change, helping predict future performance and inform strategic decisions.
Why is data visualization important in GCP reporting?
Answer: It makes complex data patterns and relationships easier to understand and communicate
Data visualization translates complex datasets into visual formats (charts, graphs, maps) that highlight patterns, trends, and outliers, making information more accessible and actionable for diverse audiences.