In managing a data project, what is a 'data roadmap' and why is it important?
- It focuses on data storage infrastructure
- It is a strategy for data security implementation
- It is a visual representation of data flows within the organization
- It outlines the project timeline and milestones related to data initiatives
A data roadmap in data project management outlines the project timeline, milestones, and key activities related to data initiatives. It provides a strategic view, helping teams understand the sequence of tasks and dependencies. It is not specifically about data security or storage infrastructure.
If x = [10, 20, 30, 40, 50], what is the output of print(x[-2])?
- 20
- 30
- 40
- 50
The output is the element at the index -2 in the list, which is 40. Negative indexing counts elements from the end of the list.
The function ________ is used in R to create user-defined functions.
- create_function()
- define_function()
- function()
- user_function()
In R, the function() keyword is used to create user-defined functions. It is followed by a set of parentheses that can contain function arguments, and then the function body is enclosed in curly braces.
In dplyr, which function combines two data frames horizontally?
- bind_rows()
- cbind()
- combine()
- merge()
In dplyr, the bind_rows() function is used to combine two data frames horizontally. It stacks the rows of the second data frame below the first, assuming the columns have the same names and types. merge() is used for more complex merging, and cbind() is a base R function for column binding. combine() is not a valid function in this context.
When analyzing a case study for a logistics company, which key performance indicator (KPI) is most relevant for assessing delivery efficiency?
- Customer Acquisition Cost
- Employee Satisfaction Score
- On-time Delivery Rate
- Return on Investment (ROI)
The On-time Delivery Rate is the most relevant KPI for assessing delivery efficiency in a logistics company. It measures the percentage of deliveries that are made on time, reflecting the company's ability to meet customer expectations regarding delivery timelines.
_______ is a technique used to handle imbalanced datasets in predictive model training.
- K-Means Clustering
- Mean Imputation
- Principal Component Analysis
- SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is a technique used to handle imbalanced datasets in predictive model training. It generates synthetic samples for the minority class to balance the dataset and improve the model's performance on minority class instances.
A data warehouse that is designed to focus on a specific business area or department is called a _______.
- Data Cluster
- Data Mart
- Data Silo
- Data Warehouse
A Data Mart is a subset of a data warehouse that is designed to focus on a specific business area or department. It contains a more specialized set of data that is relevant to a particular group of users.
During the transform phase of ETL, what is a key task performed on the data?
- Cleaning and restructuring
- Data extraction
- Data loading
- Indexing
In the transform phase of ETL (Extract, Transform, Load), a key task is cleaning and restructuring the data. This involves operations such as filtering, aggregating, and transforming the data to make it suitable for the target system or database.
The _______ method combines multiple weak models to create a stronger predictive model.
- Classification
- Clustering
- Ensemble
- Regression
The Ensemble method combines multiple weak models (such as decision trees) to create a more robust and accurate predictive model. This approach aims to reduce overfitting and improve generalization.
For a project requiring quick data exploration and visualization of Big Data, which tool would be most effective?
- Apache Spark
- Hadoop
- MongoDB
- Tableau
Tableau is a powerful data visualization tool that excels in quick data exploration and visualization. It allows users to create interactive and insightful visualizations from large datasets, making it an effective choice for projects requiring rapid exploration of Big Data.