Traditional RDBMS systems are often used for _______ applications where short, quick transactions are common.
- Analytics
- OLAP
- OLTP
- Reporting
Traditional Relational Database Management Systems (RDBMS) are commonly used for Online Transaction Processing (OLTP) applications. OLTP systems are optimized for short, quick transactions, such as order processing or record updates, where data integrity and consistency are paramount.
You are designing a database for a retail company that wants to keep track of daily price changes for products. What type of SCD would be most appropriate to capture this requirement?
- SCD Type 1
- SCD Type 2
- SCD Type 3
- SCD Type 4
For capturing daily price changes in a retail database, the most appropriate choice would be SCD Type 2, which allows for the historical tracking of changes to product prices. This type maintains a history of changes, making it ideal for scenarios where you need to preserve historical data. SCD Type 1 overwrites existing data, SCD Type 3 adds new records for changes, and SCD Type 4 is more complex and less commonly used.
Which process involves removing old or obsolete data from the data warehouse to free up storage space?
- Data Encryption
- Data Integration
- Data Masking
- Data Purging
Data purging is the process of removing old or obsolete data from the data warehouse to free up storage space. This is essential for maintaining the efficiency and performance of the data warehouse by preventing it from becoming cluttered with outdated information.
A database design that aims to improve performance by grouping data together at the expense of redundancy is called _______.
- Data Duplication
- Denormalization
- Entity-Relationship Modeling
- Normalization
Denormalization is a database design technique where data is deliberately duplicated or grouped together to improve query performance. While it may lead to some data redundancy, it can significantly enhance data retrieval speed, making it useful in data warehousing scenarios.
Which of the following best describes a scenario where a full load would be preferred over an incremental load?
- When you need to maintain historical data in the data warehouse
- When you need to update the warehouse frequently
- When you want to keep storage costs low
- When you want to reduce data processing time
A full load is preferred over an incremental load when you need to maintain historical data in the data warehouse. Incremental loads are typically used for efficiency, but when historical data must be preserved, a full load is necessary to capture all records accurately.
After loading data into a data warehouse, analysts find discrepancies in sales data. The ETL team is asked to trace back the origin of this data to verify its accuracy. What ETL concept will assist in this tracing process?
- Data Cleansing
- Data Profiling
- Data Staging
- Data Transformation
"Data Profiling" is a critical ETL concept that assists in understanding and analyzing the data quality, structure, and content. It helps in identifying discrepancies, anomalies, and inconsistencies in the data, which would be useful in tracing back the origin of data discrepancies in the sales data.
In the context of BI, what does OLAP stand for?
- Online Analytical Processing
- Open Language for Analyzing Processes
- Operational Logistics and Analysis Platform
- Overlapping Layers of Analytical Performance
In the context of Business Intelligence (BI), OLAP stands for "Online Analytical Processing." OLAP is a technology used for data analysis, allowing users to interactively explore and analyze multidimensional data to gain insights and make data-driven decisions.
Big Data solutions often utilize _______ processing, a model where large datasets are processed in parallel across a distributed compute environment.
- Linear
- Parallel
- Sequential
- Serial
Big Data solutions make extensive use of "Parallel" processing, which involves processing large datasets simultaneously across a distributed compute environment. This approach significantly enhances processing speed and efficiency when dealing with vast amounts of data.
Which of the following techniques involves pre-aggregating data to improve the performance of subsequent queries in the ETL process?
- Data Deduplication
- Data Profiling
- Data Sampling
- Data Summarization
Data summarization involves pre-aggregating or summarizing data, usually at a higher level of granularity, to improve query performance in the ETL process. This technique reduces the amount of data that needs to be processed during queries, resulting in faster and more efficient data retrieval.
What is a primary benefit of Distributed Data Warehousing?
- Enhanced query performance
- Improved data security
- Lower initial cost
- Reduced data redundancy
One of the primary benefits of Distributed Data Warehousing is improved query performance. By distributing data across multiple servers and nodes, queries can be processed in parallel, resulting in faster response times and better performance for analytical tasks.