How does a DBMS ensure data integrity?
- By allowing concurrent access to data
- By compressing data to save space
- By enforcing constraints such as primary keys and foreign keys
- By storing data in a single flat file
Data integrity in a DBMS is ensured by enforcing constraints like primary keys and foreign keys. These constraints maintain the accuracy and consistency of data by preventing invalid or inconsistent entries.
Cloud-based analytics platforms often use _______ technology to provide real-time data processing and analytics.
- Batch
- Distributed
- Parallel
- Streaming
Cloud-based analytics platforms often leverage streaming technology to process and analyze data in real-time, allowing for timely insights and decision-making. Streaming technology enables the continuous flow of data for immediate processing.
The process of transforming raw data into meaningful insights using BI tools is known as _________.
- Business Intelligence
- Data Analysis
- Data Mining
- Data Transformation
The process of transforming raw data into meaningful insights using BI tools is known as Business Intelligence (BI). This involves various activities, including data extraction, transformation, loading, analysis, and visualization, to derive valuable insights for decision-making. Data Analysis and Data Mining are components of BI, while Data Transformation is a specific step within the BI process.
f you need to continuously monitor and update data from a social media platform, which API feature should be your focus?
- OAuth Authentication
- Rate Limiting
- Swagger Documentation
- Webhooks
Webhooks allow real-time data updates by triggering events when there are changes on the social media platform. Rate Limiting is more related to controlling the number of requests, OAuth Authentication is for secure authorization, and Swagger Documentation is a tool for API documentation.
What role does 'data mart' play within a larger data warehousing strategy?
- It is a subset of a data warehouse, focusing on specific business functions or user groups.
- It is an alternative term for a data warehouse.
- It is only used for storing historical data.
- It serves as the central repository for all organizational data.
A 'data mart' is a subset of a data warehouse, designed to serve the specific needs of a particular business function or user group. It allows for a more targeted approach to data analysis and reporting within a larger data warehousing strategy.
The ________ step in ETL involves the extraction of data from various sources.
- Extraction
- Loading
- Staging
- Transformation
The Extraction step in the ETL process involves pulling data from various sources such as databases, flat files, or APIs. This data is then prepared for further processing in the ETL pipeline.
What is the purpose of the apply() function in R?
- To apply a function to a single element of a vector.
- To apply a machine learning algorithm.
- To apply a specified function over the rows or columns of a matrix or data frame.
- To apply a statistical test to the data.
The apply() function in R is used to apply a specified function over the rows or columns of a matrix or data frame. It provides a flexible way to perform operations on data in a structured manner.
In data scraping, what type of HTML element attribute is commonly used to identify specific data points?
- Class
- Href
- ID
- Style
In data scraping, the ID attribute of HTML elements is commonly used to identify specific data points. IDs should be unique within a page, making them effective markers for locating and extracting targeted information during web scraping.
For advanced data analysis, Excel's _______ tool allows integration with various programming languages like Python.
- Power Pivot
- Power Query
- Scenario Manager
- Solver
Excel's Power Pivot tool facilitates advanced data analysis by allowing integration with various programming languages like Python. It enables users to create sophisticated data models and perform complex analyses.
How does Agile methodology differ in its application in data projects compared to traditional software development projects?
- Agile is more iterative and adaptable, allowing for continuous feedback and adjustments based on evolving data requirements.
- Agile is only applicable to small-scale data projects, not suitable for large datasets.
- Agile places less emphasis on collaboration and communication, which is crucial in data projects.
- Agile strictly follows a fixed plan and timeline, making it less suitable for the dynamic nature of data projects.
Agile methodology in data projects is characterized by its adaptability and iterative nature, allowing for continuous adjustments based on evolving data requirements. This flexibility contrasts with the more rigid structure of traditional software development projects.