Given def process(item): return item * item; items = [1, 2, 3, 4]; result = map(process, items); print(list(result)), what will be the output?
- [1, 2, 3, 4]
- [1, 4, 9, 16]
- [1, 8, 27, 64]
- [2, 4, 6, 8]
The map function applies the process function to each element in items, squaring each element. The output is [1, 4, 9, 16].
What are the ethical considerations in data-driven decision making?
- Data-driven decisions are always ethical
- Ethical considerations are irrelevant in data-driven decision making
- Limited impact on individuals' rights
- Privacy concerns and data bias
Ethical considerations in data-driven decision making include addressing privacy concerns, mitigating data bias, and ensuring fair and unbiased decision outcomes. Organizations need to be mindful of potential ethical challenges to make responsible and equitable decisions.
Given def check(x): return x > 5; print(list(filter(check, [3, 4, 5, 6, 7]))), what is the output?
- [3, 4, 5, 6, 7]
- [3, 4, 5]
- [6, 7]
- [6]
The filter function applies the check function to each element in the list [3, 4, 5, 6, 7] and returns only those for which check returns True. In this case, elements greater than 5 are [6, 7], so the output is [6, 7].
What is the primary goal of data mining in a business context?
- Calculating basic statistics
- Creating data visualizations
- Discovering hidden patterns and relationships in large datasets
- Storing data securely
The primary goal of data mining in a business context is to discover hidden patterns and relationships within large datasets. This involves the use of various techniques to extract valuable insights that can inform business decisions.
In SQL, the ________ function is used to return the number of rows that match a specified criterion.
- AVG
- COUNT
- MAX
- SUM
The COUNT function in SQL is used to return the number of rows that match a specified criterion. It is often used in combination with other SQL clauses to perform aggregate functions on data.
What is the primary goal of data governance in an organization?
- Defining and enforcing data standards
- Enhancing data processing speed
- Ensuring data security and confidentiality
- Maximizing data storage capacity
The primary goal of data governance is to define and enforce data standards within an organization. This involves establishing processes, policies, and guidelines for managing data to ensure its quality, security, and compliance.
For a healthcare provider looking to predict patient readmissions, which feature selection technique would be most effective?
- Chi-square Test
- Principal Component Analysis
- Recursive Feature Elimination
- T-test
Recursive Feature Elimination (RFE) is a suitable technique for selecting features in healthcare data when predicting patient readmissions. RFE iteratively removes the least important features, helping to identify the most relevant variables for the prediction task. Principal Component Analysis, Chi-square Test, and T-test may be useful in other contexts but may not address the specific needs of predicting patient readmissions.
How does Hadoop's HDFS differ from traditional file systems?
- HDFS breaks files into blocks and distributes them across a cluster for parallel processing.
- HDFS is designed only for small-scale data storage.
- HDFS supports real-time processing of data.
- Traditional file systems use a distributed architecture similar to HDFS.
Hadoop Distributed File System (HDFS) breaks large files into smaller blocks and distributes them across a cluster of machines. This enables parallel processing and fault tolerance, which are not characteristics of traditional file systems.
Which basic data structure operates on the principle of “First In, First Out” (FIFO)?
- Linked List
- Queue
- Stack
- Tree
A Queue operates on the principle of "First In, First Out" (FIFO), meaning that the first element added is the first one to be removed. This makes it suitable for scenarios where elements are processed in the order they are added, such as in print spooling or task scheduling.
When receiving critical feedback on their data analysis, a professional data analyst should:
- Defend their analysis without considering the feedback.
- Disregard the feedback if it comes from non-technical stakeholders.
- Embrace the feedback as an opportunity for improvement and seek to understand specific concerns.
- Ignore the feedback and proceed with implementing their findings.
Embracing critical feedback is crucial for professional growth. A data analyst should welcome feedback, seek to understand concerns, and use it as an opportunity to enhance the quality and reliability of their analyses.