_______ thinking is a method used to explore complex problems by viewing them from different perspectives.
- Analytical
- Creative
- Critical
- Design
Design thinking is a method used to explore complex problems by viewing them from different perspectives. It emphasizes creativity and innovation in problem-solving. Critical thinking is important but doesn't necessarily focus on different perspectives in the same way as design thinking. Analytical thinking is more about breaking down problems logically, and creative thinking is more about generating novel ideas.
When integrating real-time data into a dashboard, what is a key factor to ensure data accuracy and timeliness?
- Data complexity
- Data latency
- Data storage
- Data volume
Data latency is a critical factor when integrating real-time data into a dashboard. It refers to the delay between the occurrence of an event and its reflection in the dashboard. Minimizing data latency ensures that the dashboard displays accurate and timely information.
In DBMS, what does ACID stand for in the context of transactions?
- Access, Control, Integration, Distribution
- Accuracy, Cohesion, Inheritance, Dependency
- Association, Collaboration, Inheritance, Division
- Atomicity, Consistency, Isolation, Durability
ACID stands for Atomicity, Consistency, Isolation, and Durability. These properties ensure the reliability and integrity of transactions in a database, guaranteeing that they are processed reliably.
For long-term projects, a data analyst maintains effective communication with stakeholders through regular _______.
- Data Reports
- Progress Updates
- Team Meetings
- Webinars
Regular team meetings are essential for maintaining effective communication with stakeholders in long-term projects. These meetings provide a platform to discuss progress, address concerns, and align goals among team members and stakeholders.
The process of transforming a complex query into a simpler query without changing the query result is known as SQL ________.
- Query Minimization
- Query Optimization
- Query Refactoring
- Query Simplification
SQL Query Optimization involves transforming a complex query into a simpler and more efficient form without altering the query result. It aims to improve performance and make the query more readable and maintainable.
How does 'snowflake schema' in a data warehouse improve upon the star schema?
- It adds more complexity to the data model.
- It eliminates the need for dimension tables.
- It increases the number of redundant fields in dimension tables.
- It normalizes dimension tables, reducing redundancy and improving data integrity.
The 'snowflake schema' improves upon the star schema by normalizing dimension tables, reducing redundancy, and improving data integrity. This makes the schema more flexible and scalable, allowing for efficient storage and maintenance of data in the data warehouse.
When the following is executed: data = [1, 2, 3, 4, 5]; filtered = filter(lambda x: x % 2 == 0, data); print(list(filtered)), what is the output?
- [1, 2, 3, 4, 5]
- [1, 3, 5]
- [2, 4]
- [4]
The filter function with the lambda expression filters out the even numbers from data, resulting in the output [2, 4].
In the context of dashboard design, what is the significance of the 'data-ink ratio'?
- It calculates the ratio of data points to the size of the dashboard, optimizing space utilization.
- It evaluates the ratio of data points to the ink color used, emphasizing the importance of color coding.
- It measures the ratio of data points to the total number of points on a chart, ensuring data accuracy.
- It represents the ratio of data to the total ink used in a visualization, emphasizing the importance of minimizing non-data ink.
The 'data-ink ratio' represents the proportion of ink in a visualization that conveys meaningful information. It emphasizes the importance of maximizing the ink used to represent data while minimizing non-data ink, promoting clarity and efficiency in dashboard design.
_______ is a technique in unsupervised learning used to reduce the dimensionality of data.
- Decision Trees
- K-Means Clustering
- Principal Component Analysis (PCA)
- Support Vector Machines (SVM)
Principal Component Analysis (PCA) is a technique in unsupervised learning used to reduce the dimensionality of data by transforming it into a set of linearly uncorrelated variables known as principal components.
In Excel, what is the difference between relative and absolute cell references?
- Absolute references are used for text data, and relative references are used for numeric data.
- Absolute references change automatically when a formula is copied, while relative references stay the same.
- Relative references adjust when a formula is copied to another cell, while absolute references remain constant.
- Relative references are only used in complex formulas.
The key difference is that relative references adjust when a formula is copied to another cell, whereas absolute references remain constant. This distinction is crucial for maintaining the integrity of formulas in Excel.