How should a data analyst approach the task of convincing stakeholders about a data-driven decision that goes against conventional wisdom?
- Aligning with conventional wisdom to maintain stakeholder trust.
- Avoiding discussions about the decision's data-driven nature to prevent resistance.
- Ignoring conventional wisdom and implementing the decision without stakeholder buy-in.
- Presenting a compelling narrative backed by data, highlighting the evidence supporting the decision.
Convincing stakeholders requires presenting a compelling narrative supported by data. Emphasizing the evidence and reasoning behind the decision helps build confidence and trust in the data-driven approach, even if it challenges conventional wisdom.
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.
To ensure effective data-driven decision making, data must be _______ and reliable.
- Abundant
- Accessible
- Accurate
- Adaptive
To ensure effective data-driven decision making, data must be accurate and reliable. Accuracy is crucial to avoid making decisions based on faulty information, and reliability ensures consistency in data quality.
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.