What is the main benefit of using a cloud-based data warehouse over a traditional data warehouse?
- Cost
- Performance
- Scalability
- Security
The main benefit is scalability. Cloud-based data warehouses offer the ability to scale resources up or down based on demand, providing flexibility and cost-effectiveness compared to traditional warehouses with fixed hardware.
In advanced data warehousing, ________ is used for real-time data processing and analytics.
- Columnar Storage
- Data Sharding
- In-Memory Computing
- Stream Processing
In advanced data warehousing, Stream Processing is used for real-time data processing and analytics. This technique allows for the processing of data as it is generated, enabling quick insights and analysis in real-time scenarios.
The _________ sorting algorithm is efficient for datasets that are already substantially sorted because it has minimal time complexity in best-case scenarios.
- Bubble
- Insertion
- Merge
- Quick
The Insertion sorting algorithm is efficient for datasets that are already substantially sorted because it has minimal time complexity in best-case scenarios. Its adaptive nature makes it suitable for nearly sorted data.
For a project requiring the extraction of specific data points from multiple e-commerce sites, what scraping strategy would be most effective?
- Beautiful Soup
- Headless Browsing
- Regular Expressions
- XPath
Beautiful Soup is a Python library that is effective for web scraping, particularly when dealing with HTML and XML. XPath is used for navigating XML documents, Regular Expressions for pattern matching, and Headless Browsing for automated interaction with websites.
In reporting, how is a KPI (Key Performance Indicator) different from a standard metric?
- KPIs are only relevant to financial reporting, while metrics are used in various domains.
- KPIs are strategic, focusing on critical business objectives, while metrics are more general measurements.
- Metrics are qualitative, while KPIs are quantitative.
- Metrics are short-term goals, while KPIs are long-term objectives.
KPIs are specific metrics that are crucial for measuring progress toward strategic business objectives. While metrics can cover a wide range of measurements, KPIs are more focused on key strategic goals and are vital for assessing overall performance.
_______ algorithms are often used to identify and clean duplicate data entries in large datasets.
- Clustering
- Deduplication
- Regression
- Sampling
Deduplication algorithms are specifically designed to identify and eliminate duplicate data entries within large datasets. Clustering is a broader technique for grouping similar data points, while regression is used for predicting numerical outcomes. Sampling involves selecting a subset of data for analysis.
How is skewness used to describe the shape of a data distribution?
- It measures the peak of the distribution
- It measures the spread of the distribution
- It measures the symmetry of the distribution
- It measures the tails of the distribution
Skewness is a measure of the asymmetry or skew of a distribution. A positive skewness indicates a longer right tail, while a negative skewness indicates a longer left tail.
_______ is a technique used in databases to improve performance by distributing a large database.
- Indexing
- Joins
- Normalization
- Sharding
Sharding is a technique used in databases to improve performance by horizontally partitioning and distributing a large database across multiple servers or nodes. It helps distribute the workload and enhance scalability. Joins, Normalization, and Indexing are also techniques but do not specifically focus on distributing a large database.
How does an ETL tool typically handle data from different sources with varying formats?
- Converting all data to a common format
- Data mapping and transformation
- Ignoring incompatible data
- Rejecting data from incompatible sources
ETL tools typically handle data from different sources with varying formats through data mapping and transformation. This involves creating mappings between source and target data structures, and applying transformations to ensure consistency and compatibility across the data.
What is the primary difference between classification and regression in machine learning?
- Classification and regression are essentially the same thing.
- Classification is used for predicting categorical outcomes, while regression is used for predicting numeric outcomes.
- Classification is used for predicting numeric outcomes, while regression is used for predicting categorical outcomes.
- Regression is only used for unsupervised learning tasks.
The primary difference is that classification is used for predicting categorical outcomes (e.g., class labels), while regression is used for predicting numeric outcomes (e.g., quantity). Classification answers questions like "Is this email spam or not?" whereas regression answers questions like "How much will the house sell for?"