MID 1 IMPORTANT QUESTIONS

UNIT-I
  1. What is an operating system? Describe the  Operating System functions
  2. Explain the Time- shared Operating System 
  3. Explain different categories of system calls with suitable example? 
  4. With a neat sketch, describe the services that an operating system provides to users, processes and other systems.
  5. What is the purpose of interrupts? What are the differences between a trap and an interrupt? Can traps be generated by a user program? Explain the purpose with an example.
  6. Explain briefly Layered Operating system structure with neat sketch.


UNIT-II
  1. What is a process? Explain about various fields of Process Control Block. 
  2. Define a Thread? Give the benefits of multithreading. What resources are used when a thread is created? How do they differ from those used when a process is created? 
  3. Distinguish between preemptive and non-preemptive scheduling. Explain each type with an example. 
  4. What is IPC? Explain in detail the inter process communication model 
  5. Explain briefly about different schedulers and scheduling criteria’s?

UNIT-III
  1. What is a Critical Section problem? Give the conditions that a solution to the critical section problem must satisfy.
  2. What is a semaphore? List the types of semaphores and Show that, if the wait() and signal() semaphore operations are not executed atomically, then mutual exclusion may be violated.
  3. What is synchronization? Explain how semaphores can be used to deal with n process critical section problem.
  4. Discuss Mutual-exclusion implementation with test and set() instruction. 
  5. Explain in detail about readers and writers problem of synchronization

Comments

Post a Comment

Popular posts from this blog

Tree pruning useful in decision tree induction

Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning? The decision tree built may overfit the training data. There could be too many branches, some of which may reflect anomalies in the training data due to noise or outliers. Tree pruning addresses this issue of overfitting the data by removing the least reliable branches (using statistical measures). This generally results in a more compact and reliable decision tree that is faster and more accurate in its classification of data. The drawback of using a separate set of tuples to evaluate pruning is that it may not be representative of the training tuples used to create the original decision tree. If the separate set of tuples are skewed, then using them to evaluate the pruned tree would not be a good indicator of the pruned tree’s classification accuracy. Furthermore, using a separate set of tuples to evaluate pruning means there are less tuples to use for ...
Read more

Machine Learning Syllabus

MACHINE LEARNING UNIT -I: The ingredients of machine learning, Tasks: the problems that can be solved with machine learning, Models: the output of machine learning, Features, the workhorses of machine learning. Binary classification and related tasks: Classification, Scoring and ranking, Class probability estimation UNIT-II: Beyond binary classification:  Handling more than two classes, Regression, Unsupervised and descriptive learning. Concept learning : The hypothesis space, Paths through the hypothesis space, Beyond conjunctive concepts UNIT- III: Tree models: Decision trees, Ranking and probability estimation trees, Tree learning as variance reduction. Rule models: Learning ordered rule lists, Learning unordered rule sets, Descriptive rule learning, First-order rule learning UNIT-IV: Linear models: The least-squares method, The perceptron: a heuristic learning algorithm for linear classifiers, Support vector machines, obtaining probabilities from linear cla...
Read more