In data warehouse monitoring, a(n) _______ provides a visual representation of the system's performance metrics in real-time.
- Dashboard
- Data Mart
- Data Query
- ETL Process
A dashboard is a crucial tool in data warehouse monitoring. It offers a visual representation of the system's performance metrics in real-time. Dashboards help data professionals track key performance indicators and quickly identify issues or opportunities for optimization.
A company's e-commerce website experiences sudden spikes in traffic during sales events. Which capacity planning strategy should they adopt to handle these unpredictable surges?
- Cloud Bursting
- Horizontal Scaling
- No Scaling
- Vertical Scaling
In this scenario, cloud bursting is the appropriate capacity planning strategy. It allows the company to use cloud resources to handle sudden traffic surges. When on-premises resources are insufficient, the organization can "burst" into the cloud temporarily to meet the increased demand, ensuring a seamless user experience.
A company's ETL process is experiencing performance bottlenecks during the transformation phase. They notice that multiple transformations are applied sequentially. What optimization strategy might help alleviate this issue?
- Data Deduplication
- Optimizing Data Storage
- Parallel Processing
- Vertical Scaling
To alleviate performance bottlenecks in the ETL process during the transformation phase, the company should consider implementing parallel processing. Parallel processing allows multiple transformations to occur simultaneously, which can significantly improve ETL performance by utilizing available system resources more efficiently. It reduces the time taken to complete the transformation phase.
_______ involves predicting future data warehouse load or traffic based on historical data and trends to ensure optimal performance.
- Capacity Planning
- Data Encryption
- Data Integration
- Data Modeling
Capacity planning in data warehousing involves predicting the future data warehouse load or traffic based on historical data and trends. This process helps ensure that the data warehouse infrastructure can handle increasing demands and maintain optimal performance.
A retail company wants to analyze the purchasing behavior of its customers over the last year, segmenting them based on their purchase frequency, amounts, and types of products bought. What BI functionality would be most suitable for this task?
- Data Integration
- Data Mining
- ETL (Extract, Transform, Load)
- OLAP (Online Analytical Processing)
The most suitable BI functionality for analyzing and segmenting customer purchasing behavior is Data Mining. Data Mining involves uncovering patterns, trends, and insights within large datasets, making it ideal for tasks like customer segmentation based on various factors.
A company is implementing stricter security measures for its data warehouse. They want to ensure that even if someone gains unauthorized access, the data they see is scrambled and meaningless. What approach should they take?
- Data Anonymization
- Data Encryption
- Data Masking
- Data Purging
To ensure that even if someone gains unauthorized access, the data they see is scrambled and meaningless, the company should take the approach of data anonymization. Data anonymization involves transforming data in a way that removes any identifying information, making it nearly impossible for unauthorized users to make sense of the data, even if they access it.
A retail company wants to analyze the past 10 years of transaction data to forecast future sales. They are considering big data solutions due to the volume of data. Which storage and processing model would be most suitable?
- Data Warehousing
- Hadoop Distributed File System (HDFS)
- NoSQL Database
- Relational Database
For handling vast volumes of data and conducting complex analytics, a big data solution like Hadoop Distributed File System (HDFS) is well-suited. It can store and process large-scale data efficiently, making it ideal for analyzing extensive historical transaction data.
What is the primary reason for implementing data masking in a data warehouse environment?
- To enhance data visualization
- To facilitate data migration
- To improve data loading speed
- To protect sensitive data from unauthorized access
Data masking is primarily implemented in data warehousing to safeguard sensitive data from unauthorized access. It involves replacing or concealing sensitive information with fictional or masked data while maintaining the data's format and usability for authorized users. This is crucial for compliance with data privacy regulations and protecting confidential information.
What does the term "data skewness" in data profiling refer to?
- A data visualization method
- A type of data transformation
- Data encryption technique
- The tendency of data to be unbalanced or non-uniformly distributed
"Data skewness" in data profiling refers to the tendency of data to be unbalanced or non-uniformly distributed. It indicates that the data has a skew or imbalance in its distribution, which can affect statistical analysis and modeling. Understanding skewness is crucial in data analysis and decision-making.
When a change in a dimension attribute results in marking the old record as inactive and inserting a new record with the changed data, it represents SCD type _______.
- SCD Type 1
- SCD Type 2
- SCD Type 3
- SCD Type 4
In Slowly Changing Dimension (SCD) Type 2, changes in dimension attributes are handled by marking the old record as inactive and inserting a new record with the updated data. This allows historical tracking of attribute changes.