MCQ Question | Machine learning interview Questions Part 3

Machine Learning interview Questions

Preparing for Machine learning Interview? Don’t be stressed, take our Question on Machine learning based quiz and prepare your self for the interview. 

With this Machine learning Interview Questions, we are going to you build your confidence by providing tips and trick to solve Machine learning interview questions. Here you will get Machine learning MCQ questions General ( Multiple Choice Questions ) and Answers for your next job or exam. In Machine learning MCQ questions based practice tests, there will be a series of practice tests wherein you can test your Basic question on Machine learning concepts on every Topic. 

Who should Practice these Machine learning Interview Questions based? 

• Anyone wishing to sharpen their knowledge in Machine learning 
• Anyone preparing for JOB interview question on Machine learning 

What is the Importance of Machine learning ? 

Machine learning is a revolutionary technology that’s changing how businesses and industries function across the globe in a good way. This Machine Learning interview questions, is a practice test that is focused to help people wanting to start their career in the Machine learning industry. This question on Machine Learning Bootcamp helps you assess how prepared are you for the Job Interview. 

Here, you get Machine Learning MCQ questions that test your knowledge on the technology. These Machine Learning Questions are prepared by subject matter experts and are in line with the questions you can come across in Job Interview. Take this test today! 

Generally, you need to refer a variety of books and Websites in order to cover the ocean of topics in Machine learning. To make it easy for you guys, I have collected a few Machine learning Based questions from different topics, When you solve these Question on machine learning then definitely Your confidence. will Increase. 

What you’ll learn 

• Able to Solve Machine Learning Based Question 

Are there any course requirements or prerequisites? 

• Basic knowledge of mathematics 
• Basic Knowledge of Computer Engineering 
• Basic Knowledge of Programming 

Who this Machine learning interview questions is for: 

• Students will develop a strong confidence on topic, "Machine Learning"

• Different learning methods does not include?

  A. Memorization
  B. Analogy
  C. Deduction
  D. Introduction
  
  Eplanation
  The Correct Answer is D.
  
  

  Different learning methods does not include the introduction.

• Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

  A. Decision Tree
  B. Regression
  C. Classification
  D. Random Forest
  
  Eplanation
  The Correct Answer is D.
  
  

  Random Forest

• Which of the following is a good test dataset characteristic?

  A. Large enough to yield meaningful results
  B. Is representative of the dataset as a whole
  C. Both A and B
  D. None of the above
  
  Eplanation
  The Correct Answer is C.
  
  

  Large enough to yield meaningful results, and Is representative of the dataset as a whole

• In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called ?

  A. mini-batches
  B. optimizedparameters
  C. hyperparameters
  D. superparameters
  
  Eplanation
  The Correct Answer is C.
  
  

  In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called hyperparameters.

• How do you handle missing or corrupted data in a dataset?

  A. Drop missing rows or columns
  B. Replace missing values with mean/median/mode
  C. Assign a unique category to missing values
  D. All of the obove3
  
  Eplanation
  The Correct Answer is D.
  
  

  All of the above techniques are different ways of imputing the missing values.

• When performing regression or classification, which of the following is the correct way to preprocess the data?

  A. Normalize the data -> PCA -> training
  B. PCA -> normalize PCA output -> training
  C. Normalize the data -> PCA -> normalize PCA output -> training
  D. None of the above
  
  Eplanation
  The Correct Answer is A.
  
  

  You need to always normalize the data first. If not, PCA or other techniques that are used to reduce dimensions will give different results.

• Which of the following statements about regularization is not correct?

  A. Using too large a value of lambda can cause your hypothesis to underfit the data.
  B. Using too large a value of lambda can cause your hypothesis to overfit the data
  C. Using a very large value of lambda cannot hurt the performance of your hypothesis.
  D. None of the above
  
  Eplanation
  The Correct Answer is D.
  
  

  A large value results in a large regularization penalty and therefore, a strong preference for simpler models, which can underfit the data.

• Which of the following techniques can be used for normalization in text mining?

  A. Streaming
  B. Lemmatization
  C. Stop Word Removal
  D. Both A and B
  
  Eplanation
  The Correct Answer is D.
  
  

  Strreaming, and Lemmatization

• Which of the following is a reasonable way to select the number of principal components "k"?

  A. Choose k to be the smallest value so that at least 99% of the varinace is retained.
  B. Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).
  C. Choose k to be the largest value so that 99% of the variance is retained.
  D. Use the elbow method
  
  Eplanation
  The Correct Answer is A.
  
  

  Choose k to be the smallest value so that at least 99% of the varinace is retained.

• You run gradient descent for 15 iterations with a=0.3 and compute J(theta) after each iteration. You find that the value of J(Theta) decreases quickly and then levels off. Based on this, which of the following conclusions seems most plausible?

  A. Rather than using the current value of a, use a larger value of a (say a=1.0)
  B. Rather than using the current value of a, use a smaller value of a (say a=0.1)
  C. a=0.3 is an effective choice of learning rate
  D. None of the above
  
  Eplanation
  The Correct Answer is C.
  
  

  a=0.3 is an effective choice of learning rate