[Solved] Hashing Function MCQ [Free PDF] - Objective Question Answer for Hashing Function Quiz - Download Now! (2024)

1. B-tree file
2. Indexed file
3. Hashed file
4. All these

The correct answer isHashed file

Key Points

• Hashing involves using a hash function to compute the address for a given record, which makes the process of locating, adding, or removing records very efficient.

Additional Information

• B-tree file: A balanced tree structure optimized for sorted data and efficient searches, insertions, and deletions.
• Indexed file: A file organization method that uses an index or indexes to quickly locate data without scanning the entire file

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1. B – Tree file
2. Hashed File
3. Indexed File
4. sequential File

Concept:

Hashing is file organization method which provides very fast access to records under certain search conditions. The organization is usually called a hash file. A hash function is used to derive the physical location of a record.

Explanation:

Search condition in this is a equality condition on a single field called hash field. Hash field is a key field of the file in which case it is called hash key. The idea behind hashing is to provide a function h called hash function which is applied to the hash field value of record and yields the address of the disk block in which record is stored.

A search for the record within the block can be carries out in a main memory buffer.

Hash function for a record will be as : h(k) = k mod m where k is the field value.

Two types of hashing:

1) Internal hashing: It is used whenever a group of record is accessed exclusively by using the value of one field. It is implemented by using a hash table through the use of array of records and a hash function.

2) External hashing: It is used for dynamically growing files. Hashing for disk files is called as external hashing. In this, target address is made of buckets each of which holds multiple records.

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1. Collision
2. Root
3. Foreign Key
4. Records

Concept:

A data model is a collection of concepts that can be used to describe the structure of a database and provides the necessary means to achieve the abstraction. Types of data model are : hierarchical data model, network data model and relational model.

Explanation:

Hierarchical data model: In this data model, data is organized in the form of tree in which nodes represent the records and branches of tree represent the fields. Parent node or root of three have connection with its child nodes.

During search operation in this data model, search takes place from root and then moves to the children to reach the specific record. That means, searching always starts from root. If we use hashing function for the search operation than hashing function will locate the root of the model.

Diagram for hierarchical database model:

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Data:

number of slots = 25

number of elements = 2000

Formula:

\({\rm{Load\;factor}}\left( {\rm{\alpha }} \right) = \frac{{{\rm{number\;of\;elements}}}}{{{\rm{number\;of\;slots}}}}{\rm{\;}}\)

Calculation:

\({\rm{Load\;factor}}\left( {\rm{\alpha }} \right) = \frac{{2000}}{{25}} = 80{\rm{\;}}\)

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1. 8
2. 14
3. 12
4. 11

When rehashing, we choose a table size that is roughly twice as large and prime. In our case, the appropriate new table size is 19, with a hash function

h(x) = x(mod 19).

So by using Quadratic Probing

The new locations are 9679 in bucket 8, 4371 in bucket 1, 1323 in bucket 12, 6173 in bucket 17, 4344 in bucket 13, 1989 in bucket14, and 4199 in bucket 0.

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Hashing Function Question 6

Hashing Function Question 6 Detailed Solution

Data:

number of slots = 25

number of elements = 2000

Formula:

\({\rm{Load\;factor}}\left( {\rm{\alpha }} \right) = \frac{{{\rm{number\;of\;elements}}}}{{{\rm{number\;of\;slots}}}}{\rm{\;}}\)

Calculation:

\({\rm{Load\;factor}}\left( {\rm{\alpha }} \right) = \frac{{2000}}{{25}} = 80{\rm{\;}}\)

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Hashing Function Question 7

1. Collision
2. Root
3. Foreign Key
4. Records

Hashing Function Question 7 Detailed Solution

Concept:

A data model is a collection of concepts that can be used to describe the structure of a database and provides the necessary means to achieve the abstraction. Types of data model are : hierarchical data model, network data model and relational model.

Explanation:

Hierarchical data model: In this data model, data is organized in the form of tree in which nodes represent the records and branches of tree represent the fields. Parent node or root of three have connection with its child nodes.

During search operation in this data model, search takes place from root and then moves to the children to reach the specific record. That means, searching always starts from root. If we use hashing function for the search operation than hashing function will locate the root of the model.

Diagram for hierarchical database model:

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Hashing Function Question 8

1. B – Tree file
2. Hashed File
3. Indexed File
4. sequential File

Hashing Function Question 8 Detailed Solution

Concept:

Hashing is file organization method which provides very fast access to records under certain search conditions. The organization is usually called a hash file. A hash function is used to derive the physical location of a record.

Explanation:

Search condition in this is a equality condition on a single field called hash field. Hash field is a key field of the file in which case it is called hash key. The idea behind hashing is to provide a function h called hash function which is applied to the hash field value of record and yields the address of the disk block in which record is stored.

A search for the record within the block can be carries out in a main memory buffer.

Hash function for a record will be as : h(k) = k mod m where k is the field value.

Two types of hashing:

1) Internal hashing: It is used whenever a group of record is accessed exclusively by using the value of one field. It is implemented by using a hash table through the use of array of records and a hash function.

2) External hashing: It is used for dynamically growing files. Hashing for disk files is called as external hashing. In this, target address is made of buckets each of which holds multiple records.

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Data:

number of slots = 25

number of elements = 2000

Formula:

\({\rm{Load\;factor}}\left( {\rm{\alpha }} \right) = \frac{{{\rm{number\;of\;elements}}}}{{{\rm{number\;of\;slots}}}}{\rm{\;}}\)

Calculation:

\({\rm{Load\;factor}}\left( {\rm{\alpha }} \right) = \frac{{2000}}{{25}} = 80{\rm{\;}}\)

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1. B-tree file
2. Indexed file
3. Hashed file
4. All these

The correct answer isHashed file

Key Points

• Hashing involves using a hash function to compute the address for a given record, which makes the process of locating, adding, or removing records very efficient.

Additional Information

• B-tree file: A balanced tree structure optimized for sorted data and efficient searches, insertions, and deletions.
• Indexed file: A file organization method that uses an index or indexes to quickly locate data without scanning the entire file

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1. Collision
2. Root
3. Foreign Key
4. Records

Concept:

A data model is a collection of concepts that can be used to describe the structure of a database and provides the necessary means to achieve the abstraction. Types of data model are : hierarchical data model, network data model and relational model.

Explanation:

Hierarchical data model: In this data model, data is organized in the form of tree in which nodes represent the records and branches of tree represent the fields. Parent node or root of three have connection with its child nodes.

During search operation in this data model, search takes place from root and then moves to the children to reach the specific record. That means, searching always starts from root. If we use hashing function for the search operation than hashing function will locate the root of the model.

Diagram for hierarchical database model:

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1. B – Tree file
2. Hashed File
3. Indexed File
4. sequential File

Concept:

Hashing is file organization method which provides very fast access to records under certain search conditions. The organization is usually called a hash file. A hash function is used to derive the physical location of a record.

Explanation:

Search condition in this is a equality condition on a single field called hash field. Hash field is a key field of the file in which case it is called hash key. The idea behind hashing is to provide a function h called hash function which is applied to the hash field value of record and yields the address of the disk block in which record is stored.

A search for the record within the block can be carries out in a main memory buffer.

Hash function for a record will be as : h(k) = k mod m where k is the field value.

Two types of hashing:

1) Internal hashing: It is used whenever a group of record is accessed exclusively by using the value of one field. It is implemented by using a hash table through the use of array of records and a hash function.

2) External hashing: It is used for dynamically growing files. Hashing for disk files is called as external hashing. In this, target address is made of buckets each of which holds multiple records.

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1. \(r/m\)
2. \((1-1/m)^r\)
3. \(1-(1-1/m)^r\)
4. \(1-r/m\)

The correct answer is option 3

EXPLANATION:

As the uniform hashing is applied each key is equally likely to be hashed into any one of the m slots.

Probability for a key to be hashed in one of the m slots = \(1\over m\)

Probability of keys not going to be hashed a particular slot (means the probability of remaining m-1 keys to be hashed out of m slots = \((m-1)\over m \)

Given that r keys are uniformly hashed into the table

So, the probability for r searches for r keys to be hashed into the table(means no collision occurs with the slot storing k) = \((m-1)^r\over m^r\)

Now if \(r \) keys(not equal to \(k\)) are uniformly hashed into this table then the probability that at least one collision occurs at the slot storing k is

⇒ \(1-{(m-1)^r\over m^r}\)

⇒ \(1-(1-1/m)^r\)

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1. \(\theta(n^2/m)\)
2. \(\theta(n)\)
3. \(\theta(m^2/n)\)
4. \(\theta(m)\)

A simple uniform hashing means that the probability of an element \(i\) hashing to slot \(k\) is \(1/m\), where \(m\) is the number of slots. Hence probability that two elements \(i \) and \(j\) will collide is \(1/m\).

\(\because\) Total number of pairs is = \({{n}\choose{2}}\)= \(\theta(n^2)\).

\(\therefore\) Expected number of colliding pairs = \(\theta(n^2/m)\).

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1. m/n
2. n/m
3. n^m
4. n*m

Load factor is defined as total number of elements stored per slot =n/m

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