Seasonality in time series analysis refers to repeating patterns or trends at regular intervals, such as daily, weekly, or yearly cycles. Identifying seasonality is crucial for accurate forecasting and understanding the underlying patterns in the data.

Employing root cause analysis is a critical thinking strategy used to delve into the fundamental reasons behind a complex problem. This method involves systematically identifying and addressing the underlying causes.

In a global setting, BI tools should facilitate real-time data synchronization to provide up-to-date information across regions. Standardized time zone conversion ensures consistency, while localized dashboards cater to specific regional needs.

CTEs in SQL are used to define a temporary result set that can be referred to within a SELECT, INSERT, UPDATE, or DELETE statement. They enhance readability and maintainability by breaking down complex queries into modular parts. Options 2, 3, and 4 are incorrect as they do not reflect the purpose of CTEs.

PivotTables in Excel are highly effective for analyzing trends over several months in a sales report. They allow users to summarize and visualize data, making it easier to identify patterns and trends. VLOOKUP, Goal Seek, and Sparklines serve different purposes and are not specifically designed for trend analysis.

Root Cause Analysis is an effective method for identifying and addressing the underlying reasons for high operational costs. It helps in pinpointing specific factors contributing to cost inefficiencies, allowing for targeted improvements. Other methods like Cost-Benefit Analysis, Regression, and Cluster Analysis may not offer the same level of detailed insights into operational inefficiencies.

Scroll Depth is a key metric for understanding user engagement depth on an online content platform. It indicates how far users scroll down a page, providing insights into their level of interest and engagement with the content. Average Session Duration, Bounce Rate, and CTR measure different aspects of user behavior.

ETL (Extract, Transform, Load) is the process used in a data warehouse to combine data from different sources into a single, integrated view. It involves extracting data, transforming it to fit the warehouse's schema, and loading it into the warehouse.

The use of distorted scales in maps can significantly alter the perceived message of the visualized data. It's crucial to use consistent and accurate scales to avoid misinterpretation of spatial patterns.

'Requests' is a common tool used in Python for sending HTTP requests to an API. It provides a simple and convenient way to interact with web services, making it a popular choice for tasks involving API communication.

The Spark framework is commonly used for machine learning and interactive data analysis over large datasets. While Hadoop is a distributed storage and processing framework, Spark provides a more flexible and efficient alternative for certain data processing tasks. Flink and Storm are also frameworks used in distributed data processing.

The 'git diff' command is used for a detailed examination of changes between two commits in Git. It provides a line-by-line comparison of the changes made, including additions and deletions. The 'log' command shows the commit history, 'status' displays the status of working directory changes, and 'branch' is used for managing branches.