Data Management Foundations (C175 )
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Free Data Management Foundations (C175 ) Questions
What is the foundation of information, which is the bedrock of knowledge
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Metadata
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Knowledge
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DBMS
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Data
Explanation
Correct Answer D. Data
Explanation
Data is the foundation of information. It consists of raw, unprocessed facts and figures, which, when organized and given context, become information. Information, in turn, serves as the building block of knowledge when analyzed and interpreted.
Why other options are wrong
A. Metadata is data about data, such as information describing the structure or attributes of other data. While important, it is not the foundation of information itself.
B. Knowledge is derived from information and data, not the foundation of information.
C. DBMS (Database Management System) is a tool used to manage and store data but is not the foundation of information.
What does the SQL command 'ALTER TABLE Movie Add Producer VARCHAR(50), DROP Genre, CHANGE Year ReleaseYear' do
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Updates the data in the Movie table
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Creates a new table named Movie
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Deletes the Movie table
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Adds a new column, drops a column, and changes a column name in the Movie table
Explanation
Correct Answer D. Adds a new column, drops a column, and changes a column name in the Movie table
Explanation
The ALTER TABLE command allows modifications to an existing table. In this command:
Add Producer VARCHAR(50) adds a new column named Producer with a data type of VARCHAR(50).
DROP Genre removes the column named Genre from the table.
CHANGE Year ReleaseYear renames the column Year to ReleaseYear.
Why other options are wrong
A. Updates the data in the Movie table
The ALTER TABLE command does not update data in the table. Data updates are handled using the UPDATE command.
B. Creates a new table named Movie
This command does not create a new table. Creating a table is done using the CREATE TABLE command.
C. Deletes the Movie table
The ALTER TABLE command does not delete the table. Deleting a table is handled by the DROP TABLE command.
What is a primary key selected to uniquely identify all other attribute values in any given row
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Primary
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Unique
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Composite
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Key
Explanation
Correct Answer A. Primary
Explanation
A primary key is a unique identifier for each row in a table, ensuring that all other attribute values in that row can be uniquely identified. This is crucial for maintaining data integrity and facilitating data relationships in the database.
Why other options are wrong
B. Unique: While a primary key is unique, not all unique constraints are primary keys. A unique constraint allows for unique values but does not necessarily act as the main identifier for the table.
C. Composite: A composite key is a type of primary key made up of two or more columns, but it is not synonymous with a primary key in general.
D. Key: This is a general term that does not fully describe the function of a primary key, which is to uniquely identify rows.
What does a CROSS JOIN do in a database system
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Compares columns of two tables
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Combines tables without comparing columns
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Selects all rows from both tables
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Selects only matching rows
Explanation
Correct Answer C. Selects all rows from both tables
Explanation
A CROSS JOIN produces the Cartesian product of two tables, selecting all possible combinations of rows from both tables. This join does not involve any condition or column comparison.
Why other options are wrong
A. Compares columns of two tables
A CROSS JOIN does not compare columns between tables. It simply combines all rows from both tables.
B. Combines tables without comparing columns
While a CROSS JOIN combines tables without comparing columns, its primary function is to produce a Cartesian product, not just to combine tables.
D. Selects only matching rows
A CROSS JOIN does not select only matching rows; instead, it selects all possible combinations of rows, regardless of matching criteria.
What is the condition in which each row in a table has its own unique identity
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Data Redundancy
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Entity Integrity
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Referential Integrity
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Data Integrity
Explanation
Correct Answer B. Entity Integrity
Explanation
Entity integrity is a condition where each row in a table is uniquely identifiable, usually enforced by a primary key. This ensures that no two rows have the same identity and maintains the uniqueness of each record.
Why other options are wrong
A. Data redundancy refers to unnecessary duplication of data, which is unrelated to the unique identification of rows.
C. Referential integrity ensures that foreign keys in a table reference valid rows in another table but does not address the unique identity of rows in a single table.
D. Data integrity is a broader concept that includes entity integrity but is not specific to unique row identities.
Cardinality represents the maximum number of entities that can be involved in a particular relationship
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Modality
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Attribute
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Entity
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Cardinality
Explanation
Correct Answer D. Cardinality
Explanation
Cardinality defines the maximum number of entities that can participate in a particular relationship in a database design. It is used in the entity-relationship (ER) model to specify the numerical constraints on the relationships between entities. For example, in a one-to-many relationship, the cardinality defines that one entity from the first set can be related to many entities in the second set.
Why other options are wrong
A. Modality: Modality defines the minimum number of entities that must participate in a relationship, not the maximum. It focuses on the necessity or optionality of the relationship.
B. Attribute: An attribute is a property or characteristic of an entity, not a measure of the number of entities involved in a relationship.
C. Entity: An entity represents a real-world object or concept in a database, but it does not describe the numerical constraints (cardinality) on relationships.
Which of the following is structured data
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Images
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Email Attachments
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Records in a database
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Files
Explanation
Correct Answer C. Records in a database
Explanation
Structured data is highly organized and stored in predefined formats like rows and columns within a database. Records in a database fit this description, as they follow a schema and are easy to query using SQL.
Why other options are wrong
A. Images: Images are examples of unstructured data because they lack a predefined format and cannot be easily stored in tables or queried using SQL.
B. Email Attachments: Email attachments can contain diverse types of unstructured data (e.g., text, images, videos), which are not organized in a tabular format.
D. Files: Files can be either structured or unstructured depending on their content, but the term "files" in general does not inherently mean structured data.
What type of attribute is a Social Security number considered to be
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Composite
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Multivalued
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Single valued
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Derived
Explanation
Correct Answer C. Single valued
Explanation
A Social Security number is considered a single-valued attribute because it holds a unique, indivisible value for each individual. It cannot be split further or have multiple values associated with one person.
Why other options are wrong
A. Composite:
A composite attribute can be divided into smaller subparts, such as an address (street, city, state). A Social Security number is indivisible, so it is not composite.
B. Multivalued:
A multivalued attribute can have multiple values, such as a person's phone numbers. A Social Security number only has one value per individual, so it is not multivalued.
D. Derived:
A derived attribute is calculated from other attributes, such as age derived from a birthdate. A Social Security number is a stored value and not derived.
What does granularity refer to in the context of database tables
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The number of rows in a table
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The size of a table
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The level of detail represented by the values stored in a row
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The number of attributes represented in a table
Explanation
Correct Answer C. The level of detail represented by the values stored in a row
Explanation
Granularity refers to the level of detail or specificity represented by the values in a database table row. Higher granularity means more detailed data, whereas lower granularity indicates more aggregated or summarized data.
Why other options are wrong
A. The number of rows in a table: Granularity is not concerned with the count of rows but rather the detail level of the data in each row. Rows can vary regardless of granularity.
B. The size of a table: This relates to storage or physical attributes of the database, not the conceptual level of data detail that granularity represents.
D. The number of attributes represented in a table: While attributes (columns) describe data, granularity focuses on the detail captured by each row's data, not the count of attributes.
A derived attribute
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Must be stored physically within the database
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Need not be physically stored within the database
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Has many values
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Must be based on the value of three or more attributes
Explanation
Correct Answer B. Need not be physically stored within the database
Explanation
A derived attribute is an attribute whose value is calculated from other attributes. Since it can be generated at query time, it does not need to be stored physically within the database, reducing storage redundancy.
Why other options are wrong
A. Must be stored physically within the database.
Storing derived attributes physically in the database is unnecessary because they can be calculated dynamically. Doing so increases the risk of data redundancy and inconsistency if the derived value does not update with changes in base attributes.
C. Has many values"
Derived attributes typically have a single value derived from other attributes. The phrase "has many values" applies to multivalued attributes, not derived attributes.
D. Must be based on the value of three or more attributes
Derived attributes can be calculated from one or more attributes but are not required to depend on three or more attributes. The number of attributes involved depends on the specialculafic ction.
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Data Management Foundations (C175 )
1. Introduction to Data Management
1.1 Definition and Importance
Data management refers to the process of collecting, storing, organizing, maintaining, and utilizing data efficiently and securely. It is a critical aspect of any organization as it ensures that data is accurate, accessible, and secure. Effective data management enables organizations to make informed decisions, improve operational efficiency, and gain a competitive advantage.
Example: A retail company uses data management to track inventory levels, customer preferences, and sales trends. This data helps the company optimize its supply chain, personalize marketing efforts, and increase sales.
1.2 Key Concepts in Data Management
- Data Governance: The framework for managing data assets, including policies, standards, and procedures.
- Data Quality: The accuracy, completeness, and consistency of data.
- Data Integration: The process of combining data from different sources to provide a unified view.
- Data Security: The protection of data from unauthorized access and breaches.
The data management lifecycle consists of several stages:
- Data Collection: Gathering data from various sources.
- Data Storage: Storing data in databases or data warehouses.
- Data Processing: Cleaning, transforming, and analyzing data.
- Data Usage: Utilizing data for decision-making and reporting.
- Data Archiving: Storing data that is no longer actively used.
- Data Disposal: Safely deleting data that is no longer needed.
2. Data Models and Database Design
2.1 Relational Data Model
The relational data model organizes data into tables (relations) consisting of rows (tuples) and columns (attributes). It is based on the principles of set theory and relational algebra.
2.2 Entity-Relationship Model
The Entity-Relationship (ER) model is a conceptual data model that represents data as entities and relationships. Entities are objects or concepts, and relationships define how entities are related.
2.3 Normalization
Normalization is the process of organizing data to reduce redundancy and improve data integrity. It involves decomposing tables into smaller, related tables
2.4 Denormalization
Denormalization is the process of combining tables to improve read performance, often at the expense of write performance and data redundancy.
Example: In a data warehouse, denormalization might be used to create a single table that combines customer and order information for faster querying.
3. Structured Query Language (SQL)
3.1 SQL Basics
SQL is the standard language for interacting with relational databases. It includes commands for querying, updating, and managing data.
Example: The SQL query SELECT * FROM Customers; retrieves all records from the Customers table.
3.2 Data Definition Language (DDL)
DDL commands are used to define and modify database structures.
Example: The SQL command CREATE TABLE Students (StudentID INT, Name VARCHAR(50), Major VARCHAR(50)); creates a new table named Students.
3.3 Data Manipulation Language (DML)
DML commands are used to manipulate data within tables.
Example: The SQL command INSERT INTO Students (StudentID, Name, Major) VALUES (1, 'John Doe', 'Computer Science'); adds a new record to the Students table.
3.4 Advanced SQL Queries
Advanced SQL queries include joins, subqueries, and aggregate functions.
Example: The SQL query SELECT Students.Name, Courses.Title FROM Students JOIN Enrollments ON Students.StudentID = Enrollments.StudentID JOIN Courses ON Enrollments.CourseID = Courses.CourseID; retrieves the names of students and the titles of the courses they are enrolled in.
4. Data Storage and Retrieval
4.1 File Systems vs. Databases
File systems store data in files and directories, while databases store data in structured formats with relationships between data elements.
Example: A file system might store customer data in a text file, while a database would store it in a Customers table with relationships to Orders and Payments tables.
4.2 Indexing and Hashing
Indexing and hashing are techniques used to speed up data retrieval.
Example: An index on the CustomerID column in the Orders table allows for faster retrieval of orders for a specific customer.
4.3 Query Optimization
Query optimization involves improving the performance of database queries.
Example: Rewriting a query to use an index or reducing the number of joins can improve performance.
5. Data Security and Integrity
5.1 Data Security Concepts
Data security involves protecting data from unauthorized access and breaches.
Example: Encryption, access controls, and auditing are common data security measures.
5.2 Data Integrity Constraints
Data integrity constraints ensure the accuracy and consistency of data.
Example: A primary key constraint ensures that each record in a table is unique.
5.3 Backup and Recovery
Backup and recovery processes ensure that data can be restored in the event of a loss.
Example: Regular backups and a disaster recovery plan are essential for data protection.
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