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.
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.
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 dplyr, to perform operations on multiple columns at once, the _______ function is used.
- across()
- group_by()
- mutate()
- summarize()
The across() function in dplyr is used to perform operations on multiple columns simultaneously. It is particularly useful when you want to apply the same operation to multiple columns in a data frame.
What advanced metric is used to assess the long-term value of a customer to a business?
- Cost per Acquisition (CPA)
- Customer Lifetime Value (CLV)
- Net Promoter Score (NPS)
- Return on Investment (ROI)
Customer Lifetime Value (CLV) is a key metric in assessing the long-term value of a customer to a business. It represents the total revenue a business expects to earn from a customer throughout their entire relationship. ROI, NPS, and CPA are important metrics but focus on different aspects.
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.