_______ 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.
How does logical modeling differ from physical modeling in terms of its audience or target stakeholders?
- Logical modeling and physical modeling have the same target audience.
- Logical modeling deals with data visualization, while physical modeling deals with data analysis.
- Logical modeling focuses on data structures, while physical modeling focuses on business processes.
- Logical modeling targets business users, while physical modeling targets IT professionals.
Logical modeling is primarily intended for business users and stakeholders who want to understand the data in a business context. It focuses on data structure and representation without considering technical implementation details. In contrast, physical modeling is aimed at IT professionals who design the actual database systems and consider implementation specifics.
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.
In big data analytics, the process of analyzing current and historical data to make predictions about future events is known as _______.
- Data Aggregation
- Data Retrieval
- Descriptive Analytics
- Predictive Analytics
In big data analytics, the process of analyzing current and historical data to make predictions about future events is known as "Predictive Analytics." Predictive analytics uses statistical algorithms and machine learning techniques to identify patterns and trends in data, helping organizations make informed decisions and forecasts.
Which approach in ERP involves tailoring the software to fit the specific needs and processes of an organization, often leading to longer implementation times?
- Cloud-based ERP
- Customized ERP
- Off-the-shelf ERP
- Open-source ERP
The approach in ERP that involves tailoring the software to fit the specific needs and processes of an organization is called "Customized ERP." This approach can lead to longer implementation times as it requires the software to be configured or developed to align with the unique requirements of the organization, ensuring a closer fit to their business processes.
In a star schema, a fact table typically contains the measures and foreign keys to the _______ tables.
- Aggregate
- Dimension
- Fact
- Primary
In a star schema, the fact table contains the measures (quantitative data) and foreign keys that connect to dimension tables. Dimension tables hold descriptive information about the data, so the foreign keys in the fact table point to the dimension tables, allowing you to analyze the measures in context.