What is the main benefit of using a cloud-based data warehouse over a traditional data warehouse?
- Cost
- Performance
- Scalability
- Security
The main benefit is scalability. Cloud-based data warehouses offer the ability to scale resources up or down based on demand, providing flexibility and cost-effectiveness compared to traditional warehouses with fixed hardware.
During a presentation, if a data analyst encounters a question they cannot answer, they should:
- Acknowledge the question and commit to finding the answer later
- Express frustration with the question
- Ignore the question and move on to the next topic
- Make up an answer to maintain confidence
If a data analyst encounters a question they cannot answer during a presentation, it's best to acknowledge it, express a willingness to find the answer later, and move on. Honesty and commitment to follow up build credibility.
________ analysis is a data mining technique used for exploring large text datasets to discover meaningful patterns.
- Clustering
- Regression
- Sentiment
- Text
Sentiment analysis is a data mining technique specifically used for exploring large text datasets to determine the sentiment expressed in the text. It helps in understanding opinions, emotions, and trends within textual data. Text, Clustering, and Regression are other techniques with different applications.
In time series analysis, what term is used for a trend that repeats over a fixed period?
- Cyclical Trend
- Linear Trend
- Periodic Trend
- Seasonal Trend
A trend that repeats over a fixed period is referred to as a Seasonal Trend in time series analysis. It corresponds to patterns that recur regularly within a specific time frame, such as seasons in a year.
The technique of _______ is used in data-driven decision making to understand the potential outcomes of different decisions.
- Clustering
- Optimization
- Regression
- Simulation
The technique of simulation is employed in data-driven decision making to simulate various scenarios and understand the potential outcomes of different decisions. This helps in assessing the impact of different choices before making a decision.
_______ analysis is a technique used to dissect complex data sets to understand underlying patterns and relationships.
- Descriptive
- Diagnostic
- Exploratory
- Predictive
Exploratory analysis is a technique used to dissect complex data sets. It focuses on discovering underlying patterns, relationships, and trends that may not be immediately apparent. This method is particularly useful in the early stages of data analysis.
Which algorithm would be most appropriate for forecasting future sales based on historical data?
- Decision Trees
- K-Means Clustering
- Linear Regression
- Naive Bayes
Linear Regression is a suitable algorithm for forecasting future sales based on historical data. It models the relationship between the dependent variable (sales) and one or more independent variables (time, marketing spend, etc.), making predictions based on historical patterns.
For a sales analysis report showing performance over several years, which advanced visualization tool would be most effective?
- Heat Map
- Line Chart
- Treemap
- Waterfall Chart
In the context of a sales analysis report spanning several years, a Line Chart is an effective visualization tool. It allows the viewer to observe trends and changes in sales performance over time, making it suitable for time-series data.
What is the primary challenge in using time series data for predictive modeling?
- Dealing with missing values
- Ensuring the data is stationary
- Handling seasonality in the data
- Incorporating external factors
The primary challenge in time series predictive modeling is achieving stationarity, meaning that the statistical properties of the data (e.g., mean and variance) remain constant over time. Stationarity is crucial for accurate modeling and forecasting.
The ability of a BI tool to handle _________ data sources is crucial for organizations with diverse data ecosystems.
- Cloud-based
- Semi-Structured
- Structured
- Unstructured
The ability to handle Semi-Structured data sources is crucial for organizations with diverse data ecosystems. Semi-Structured data includes formats like JSON or XML, and a capable BI tool should support extracting insights from such sources.