What is the impact of big data technologies on data-driven decision making?
- Enhanced scalability and processing speed
- Increased data security concerns
- Limited applicability to small datasets
- Reduced need for data analysis
Big data technologies, with enhanced scalability and processing speed, enable organizations to process and analyze vast amounts of data quickly. This facilitates more informed and timely data-driven decision making.
In a scenario where a business needs to perform complex data analyses with minimal upfront investment, which cloud service would be most appropriate?
- AWS Glue
- AWS Redshift
- Azure Data Lake Analytics
- Google BigQuery
Google BigQuery would be most appropriate. It is a serverless, highly scalable, and cost-effective data warehouse that allows complex data analyses with minimal upfront investment.
When dealing with time series data, which type of data structure is most efficient for sequential access and why?
- Array
- Linked List
- Queue
- Stack
An array is most efficient for sequential access in time series data. This is because arrays provide direct access to elements based on their indices, making it faster to retrieve data points in sequential order. Linked lists involve traversal, while queues and stacks are not as suitable for direct access.
To combine rows from two or more tables based on a related column, you use a SQL ________.
- COMBINE
- JOIN
- MERGE
- UNION
In SQL, the JOIN keyword is used to combine rows from two or more tables based on a related column. It allows you to retrieve data from multiple tables based on a related column between them.
How does 'commit' function in Git?
- To copy changes from the local repository to the remote repository
- To delete files from the repository
- To merge branches in Git
- To save changes in the local repository
In Git, 'commit' is used to save changes made to the local repository. It creates a snapshot of the changes, making it possible to track the project's history and revert to previous states if needed. Committing is a crucial step in the version control process.
What does the acronym KPI stand for in business analytics?
- Key Performance Indicator
- Key Performance Insight
- Key Progress Indicator
- Key Project Insight
KPI stands for Key Performance Indicator. These are measurable values that demonstrate how effectively a company is achieving key business objectives. KPIs help in evaluating performance and making informed decisions.
The process of continuously checking and ensuring the quality of data throughout the project life cycle is known as _________.
- Data Mining
- Data Quality Management
- Data Validation
- Data Wrangling
Data Quality Management involves continuously checking and ensuring the quality of data throughout the project life cycle. It includes processes to identify and correct errors, inconsistencies, and inaccuracies in the data.
To change the structure of a database table, the _______ SQL statement is used.
- ALTER
- CHANGE
- MODIFY
- UPDATE
The ALTER SQL statement is used to modify the structure of a database table. It can be used to add, delete, or modify columns, as well as change data types or constraints.
In a situation where data consistency is crucial, and you have multiple related update operations, how would you manage these operations in SQL?
- Apply triggers
- Use indexes
- Use transactions
- Utilize stored procedures
To ensure data consistency in situations involving multiple related update operations, transactions are used in SQL. Transactions allow you to group multiple SQL statements into a single, atomic operation, ensuring that all changes are applied or none at all.
In the context of data mining, how is 'ensemble learning' best described?
- A technique that combines predictions from multiple models to improve accuracy and robustness.
- Using algorithms specifically designed for mining ensemble datasets.
- Focusing on individual model predictions for better interpretability.
- Ignoring the diversity of models for simplicity.
Ensemble Learning involves combining predictions from multiple models to enhance overall accuracy and reliability. It leverages the strengths of different models and reduces the risk of relying on a single model's limitations. The other options do not accurately describe ensemble learning.