Relational database management systems mcq quiz with answers 18

MCQ on various aspects in DBMS including database schedules, indexing, data storage, normalization, database views, query optimizers and multivalued dependency questions with answers, RDBMS Multiple Choice Questions with Answers

Database management systems - MCQ exam questions

 

1. Consider a B+ tree of order 100. One of the non-leaf nodes has 95 pointers. How many keys will that node contain?

a) 95

b) 94

c) 100

d) 47

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Ans : (b)

Answer: (b) 94 The number of keys in a non-leaf node is 1 less the number of pointers in that node. Few properties of B+ tree B+ tree is a balanced binary search tree, hence all leaf nodes are at the same level. That is, all leaf nodes are at the same height from the root. If the root node is not a leaf, then it has at least two children. In a tree of order (branching factor) m, each node, except root and leaf, can have a maximum of m children and at least m/2 children. Each node can contain a maximum of m - 1 keys and a minimum of ⌈m/2⌉ - 1 keys. The number of keys in a non-leaf node is p-1 where p is the number of pointers.

 

2. Consider a B+ tree of order 100. One of the leaf nodes has 90 pointers. How many keys will that node contain?

a) 90

b) 100

c) 89

d) 91

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Ans : (c)

Answer: (c) 89 A leaf node with 𝑛 keys needs 𝑛+1 pointers (𝑛 pointers to the actual data and 1 pointer to its sibling). In this question, it is given that the total number of pointers in a leaf node is 90. That is 89 for actual data and 1 for sibling node.

 

3. Consider a database file that consists of 1000 fixed-length records. The size of each record is 300 bytes. This file has to be stored in disk blocks of size 1024 bytes, with no spanning of records. How many blocks will be needed to store this file?

a) 300

b) 1000

c) 334

d) 332

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Ans : (c)

Answer: (c) 334 For unspanned records, the blocking factor (bfr) = floor(B/R). Here, B is the disk block size and R is the record size. Hence, bfr = floor(1024/300) = floor(3.413) = 3. Number of blocks needed to store = ceiling(r/bfr). Here, r is the number of records in the table. Number of blocks needed = ceiling(1000/3) = ceiling(333.33) = 334.

 

4. Which of the following is/are most suitable for the given schedule S₁?

Schedule S₁ = W₁(X), W₂(Y), W₃(Z), C₁, R₃(X), C₂, R₃(Y), C₃

Here, W and R represent write and read operations respectively, numbers in the suffix represents the transaction id, letters inside bracket represent data items, and C₁, C₂, C₃ are commit instructions.

a) Conflict-serializable schedule

b) Recoverable schedule

c) Not conflict-serializable schedule

d) Not-recoverable schedule

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Ans : (a) and (b)

Answer: (a) Conflict-serializable schedule and (b) Recoverable schedule Transaction T3 reads data items X and Y that are written by transaction T1 and T2 respectively. Schedule S1 is recoverable because the commit instruction of T1 and T2 appears before that of T3. The precedence graph for S1 does not have any cycles. Hence, S1 is conflict-serializable. Precedence graph: Recoverable schedule A recoverable schedule is one where, for each pair of transactions Ti and Tj such that Tj reads a data item previously written by Ti, the commit operation of Ti appears before the commit operation of Tj.

 

5. Which of the following is/are most suitable for the given schedule S₂?

Schedule S₂ = R₁(X), W₂(X), W₂(Y), C₂, R₁(Y), C₁

a) Conflict-serializable schedule

b) Recoverable schedule

c) Not conflict-serializable schedule

d) Not-recoverable schedule

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Ans : (b) and (c)

Answer: (b) Recoverable schedule and (c) Not conflict-serializable schedule This schedule is recoverable because T1 reads a data item that is written by T2 and T1 commits after T2. The schedule is not conflit-serializable because it has cycle in the precedence graph.

 

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Quiz questions with answers on DBMS introduction concepts

How many keys a leaf node can have in a B+ tree in DBMS

How many pointers a non-leaf node can have in a B+ tree in DBMS

what are the properties of B+ tree 

how many disk blocks required to store a database file of fixed size records with no spanning of records

List down all the recoverable and conflict serializable schedules 

Machine Learning Multiple Choice Questions and Answers 21

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, k-means, elbow method, decision tree, entropy calculation

Machine learning MCQ - Set 21

 

1. In terms of the bias-variance trade-off, which of the following is substantially more harmful to the test error than the training error?

a) Bias

b) Loss

c) Variance

d) Risk

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Ans : (c)

Answer: (c) Variance Training error - It is the error that you get when you run the trained model back on the training data. Test error – It is the error that you get when you run the trained model on a set of unseen data. Variance occurs when the model performs well on the trained dataset but does not do well on a dataset that it is not trained on, like a test dataset or validation dataset. Variance tells us how scattered are the predicted value from the actual value. High variance causes overfitting that implies that the algorithm models random noise present in the training data.

 

2. Which of the following learning algorithms will return a classifier if the training data is not linearly separable?

a) Hard margin SVM

b) Soft margin SVM

c) Perceptron

d) Naïve bayes

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Ans : (b)

Answer: (b) Soft margin SVM Soft margin SVM If the data set is not linearly separable (due to noise), the standard approach is to allow the decision margin to make a few mistakes (some points - outliers or noisy examples - are inside or on the wrong side of the margin). It tries to balance the trade-off between finding a line that maximizes the margin and minimizes the misclassification. [For more information, refer here] The trick Soft Margin SVM is using is very simple, it adds slack variables to the constraints of the optimization problem. By adding the slack variables, when minimizing the objective function, it is possible to satisfy the constraint even if the example does not meet the original constraint. [For more, refer here]

 

3. Modeling a classification rule directly from the input data like in logistic regression fits which of the following classification methods?

a) Discriminative classification

b) Generative classification

c) Probabilistic classification

d) All of the above

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Ans : (a)

Answer: (a) Discriminative classification Discriminative Classifiers learn the features in the input that are most useful to distinguish between the various possible classes. Discriminative classifiers directly calculates the posterior probability P(y|x) or learn a direct map from input x to the class labels. So, these models try to learn the decision boundary for the model. Generative classification In generative model, we model the conditional probability of the input x given the label y. Generative model learns the joint probability distribution P(x, y) and uses Bayes’ theorem to find the conditional probability. Probabilistic classification Probabilistic classification is the study of approximating a joint distribution with a product distribution. Bayes rule is used to estimate the conditional probability of a class label y, and then assumptions are made on the model, to decompose this probability into a product of conditional probabilities.

 

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