Which method can be used to handle missing data in a dataset?
- Data compression
- Data encryption
- Data imputation
- Data transformation
Data imputation is a method used to handle missing data in a dataset. It involves estimating or filling in the missing values using various techniques, such as mean, median, or machine learning algorithms. This ensures that the dataset remains complete for analysis and modeling.
Why is a data warehouse backup different from a regular database backup?
- Data warehouses are often larger and more complex
- Data warehouses are read-only systems
- Data warehouses store only historical data
- Data warehouses use a different backup software
Data warehouse backups differ from regular database backups because data warehouses are typically larger and more complex due to the vast amount of data they store. The backup strategies and processes for data warehouses need to accommodate the unique challenges posed by the size and complexity of these systems.
Why might a fact table contain surrogate keys that reference dimension tables?
- To improve data quality
- To reduce storage space
- To simplify query writing
- To support slowly changing dimensions
Fact tables may contain surrogate keys that reference dimension tables to support slowly changing dimensions (SCDs). Surrogate keys provide a stable reference to dimension data, even when the source dimension data changes. This is essential for historical analysis and maintaining data consistency in the data warehouse.
In ERP implementations, what is often considered a critical success factor due to its impact on user adoption and efficiency?
- Data Security
- User Training
- Hardware Specifications
- Project Documentation
In ERP implementations, user training is often considered a critical success factor. Proper training helps users understand and use the ERP system effectively, leading to higher user adoption rates and increased operational efficiency. Without adequate training, users may struggle to make the most of the system.
How does "data lineage" aid in the ETL process?
- It ensures data security during transfer
- It helps track the origin and transformation of data
- It optimizes database indexing
- It provides documentation for regulatory compliance
"Data lineage" in the ETL process is crucial for tracking the origin and transformation of data. It provides a visual representation of how data flows from source to destination, helping data professionals understand the data's journey and ensuring data quality, compliance, and troubleshooting.
You notice that certain queries are running slower over time in your data warehouse. Which strategy might help improve their performance without changing the query itself?
- Adding more data sources
- Creating appropriate indexes
- Increasing the server's CPU
- Redesigning the database schema
One way to enhance the performance of slow-running queries in a data warehouse without modifying the query itself is to create appropriate indexes. Indexes improve query execution by allowing the database system to quickly locate the required data, reducing the need for full table scans.
An organization is experiencing slower query performance during peak business hours on their traditional data warehouse system. Which solution might alleviate this problem?
- Adding more indexes to the database
- Implementing a data warehouse appliance
- Implementing data partitioning
- Scaling up the hardware resources
Slower query performance during peak business hours is often caused by excessive data volume. Data partitioning is a technique where large data sets are divided into smaller, more manageable partitions. By implementing data partitioning, the data warehouse can distribute the query workload more efficiently, resulting in faster query performance during peak times. This is a common optimization strategy for traditional data warehouses.
You are working with a dataset where city names have been entered in various formats (e.g., "NYC," "New York City," "New York"). To standardize these entries, which data cleaning technique would be most appropriate?
- Data Imputation
- Data Normalization
- One-Hot Encoding
- String Matching
When dealing with diverse formats of city names, string matching is the most suitable data cleaning technique. It involves comparing and matching strings to standardize them. This ensures that all variations of city names are transformed into a consistent format, making data analysis and aggregation more straightforward.
What is the primary function of a data warehouse?
- Data extraction
- Data processing
- Storing and organizing data for analysis
- Storing raw data
The primary function of a data warehouse is to store and organize data for analysis. It acts as a centralized repository where data from various sources is integrated, cleaned, and structured to facilitate business intelligence and reporting. Data warehouses are designed to support complex queries and reporting, providing a foundation for data-driven decision-making.
In BI reporting, what type of visualization would best represent the distribution of sales over a year?
- Bar Chart
- Line Chart
- Pie Chart
- Scatter Plot
A line chart is the best choice to represent the distribution of sales over a year. It allows you to track trends and variations in data over time, making it suitable for visualizing sales performance throughout the year.