What is a weak entity in an ERD?
- An entity that can exist independently
- An entity that cannot be uniquely identified
- An entity that is strongly related to another entity
- An entity with a single attribute
A weak entity in an ERD is one that cannot be uniquely identified by its attributes alone. It depends on a related entity (owner entity) for its existence and is represented by a double-bordered rectangle.
Scenario: You are tasked with assessing the quality of a large dataset containing customer information. Which data quality assessment technique would you prioritize to ensure that the data is accurate and reliable?
- Data auditing
- Data cleansing
- Data profiling
- Data validation
Data profiling involves analyzing the structure, content, and relationships within the dataset to identify anomalies, inconsistencies, and inaccuracies. By prioritizing data profiling, you can gain insights into the overall quality of the dataset, including missing values, duplicates, outliers, and inconsistencies, which is crucial for ensuring data accuracy and reliability.
Scenario: Your team is developing a real-time analytics application using Apache Spark. Which component of Apache Spark would you use to handle streaming data efficiently?
- GraphX
- MLlib
- Spark SQL
- Structured Streaming
Structured Streaming is a high-level API in Apache Spark that enables scalable, fault-tolerant processing of real-time data streams. It provides a DataFrame-based API, allowing developers to apply the same processing logic to both batch and streaming data, simplifying the development of real-time analytics applications and ensuring efficient handling of streaming data.
The process of ______________ involves identifying and resolving inconsistencies in data to ensure data quality.
- Data cleansing
- Data integration
- Data profiling
- Data transformation
Data cleansing is the process of identifying and resolving inconsistencies, errors, and discrepancies in data to ensure its quality before it is used for analysis or other purposes.
In an RDBMS, what is a primary key?
- A key used for encryption
- A key used for foreign key constraints
- A key used for sorting data
- A unique identifier for a row in a table
In an RDBMS, a primary key is a column or set of columns that uniquely identifies each row in a table. It ensures the uniqueness of rows and provides a way to reference individual rows in the table. Primary keys are crucial for maintaining data integrity and enforcing entity integrity constraints. Typically, primary keys are indexed to facilitate fast data retrieval and enforce uniqueness.
Which of the following best describes the primary purpose of Dimensional Modeling?
- Capturing detailed transactional data
- Designing databases for efficient querying
- Implementing data governance
- Organizing data for data warehousing
The primary purpose of Dimensional Modeling is to organize data for data warehousing purposes, making it easier to analyze and query for business intelligence and reporting needs.
The process of transforming raw data into a format suitable for analysis in a data warehouse is called ________.
- ELT (Extract, Load, Transform)
- ETL (Extract, Load, Transfer)
- ETL (Extract, Transform, Load)
- ETLT (Extract, Transform, Load, Transform)
The process of transforming raw data into a format suitable for analysis in a data warehouse is called ELT (Extract, Load, Transform). In this approach, data is first loaded into the warehouse and then transformed according to analysis requirements.
Why is it important to involve stakeholders in the data modeling process?
- To delay the project
- To gather requirements and ensure buy-in
- To keep stakeholders uninformed
- To make decisions unilaterally
It is important to involve stakeholders in the data modeling process to gather their requirements, ensure buy-in, and incorporate their insights, which ultimately leads to a database design that meets their needs.
Scenario: A critical component in your data processing pipeline has encountered a series of failures due to database overload. How would you implement a circuit-breaking mechanism to mitigate the impact on downstream systems?
- Automatically scale resources to handle increased load
- Monitor database latency and error rates
- Set thresholds for acceptable performance metrics
- Temporarily halt requests to the overloaded component
Implementing a circuit-breaking mechanism involves monitoring performance metrics such as database latency and error rates. By setting thresholds for these metrics, the system can detect when the database is overloaded and temporarily halt requests to prevent further degradation of downstream systems. This allows time for the database to recover and prevents cascading failures throughout the pipeline.
________ in data modeling tools like ERWin or Visio allows users to generate SQL scripts for creating database objects based on the designed schema.
- Data Extraction
- Forward Engineering
- Reverse Engineering
- Schema Generation
Forward Engineering in data modeling tools like ERWin or Visio enables users to generate SQL scripts for creating database objects, such as tables, views, and indexes, based on the designed schema.