Sunday, September 19, 2021

What are some questions that might be asked on an exam for the certification in data science?

Introduction

Data science is the trendiest career at present. The growing demand for data science professionals makes it a high paying stable job. Data science experts manage and gain insights from huge chunks of data, these data insights help businesses grow and enhance their products or services. The best thing is that you can start a career in data science with some professionals courses. The next step is to crack the exam or interviews to grab the opportunity. Cracking a data science interview or an exam might be a tedious task. You need to have a sound knowledge of different concepts and tasks.

To ease your preparation, we have collected some common questions on clustering, tree based models, probability, statistics, machine learning algorithms, deep learning, and many others. These questions are often asked in examinations and interviews of data science. This is a perfect blog to boost your preparation.

The questions have been divided into different sections of concepts for ease. Let’s go through them.


Clustering

The raw data needs to be categorized for analysis, clustering plays an important role in segregating unlabelled data. It helps to put similar data in groups for professionals to draw conclusions. In simple words, it helps segregate data sets into smaller groups of similar characteristics. Some questions related to clustering are:

Questions

Q 1. Show recommendation systems are a part of?

  1. Classification
  2. Regression
  3. Clustering
  4. Reinforcement learning.


Q 2. What are the features of Hierarchical Clustering in R?


Q 3. Will you get the same results for two runs of K-Mean clustering?

  1. Yes
  2. No


Q 4. Which of the clustering algorithms suffer from convergence at local optima?

  1. Diverse clustering algorithm
  2. K-Mean clustering
  3. Agglomerative clustering algorithm
  4. Diverse clustering algorithm


Q 5. How can you improve the accuracy of the Linear Regression model with clustering?

  1. By creating an input feature for cluster size as a continuous variable.
  2. By creating an input feature for cluster ids as ordinal variables.
  3. By creating separate modes for different cluster groups.
  4. By creating an input feature for cluster centroids as a continuous variable.


Q 6. What is produced by Hierarchical clustering at the end?

  1. Assignment of each point to clusters.
  2. End estimate of cluster centroids.
  3. Trees showing how much things are close to each other.
  4. All of the above.



Q 7. What is the minimum number of variables required to perform clustering?

  1. 2
  2. 0
  3. 1
  4. 5

Q 8. Is it true that K-means clustering is not deterministic?

Q 9. Which are the valid iterative strategies for treating missing values before doing clustering?

  1. Put mean with imputation
  2. Assign the nearest neighbor
  3. Expectation-Maximization algorithm
  4. All of the above.


Q 10. What is the correct sequence for the K-Means clustering algorithm using the Forgy method of initialization?

  1. Re-computing cluster centroids
  2. Assign the cluster centroids randomly
  3. Specify number of clusters
  4. Assigning each data point to the nearest cluster centroid.
  5. Re-assign each of the data points to the nearest cluster centroid.

Tree-based models

Tree-based models are used as decision trees to represent data and get target based predictions. These models yield highly accurate, stable, and easy to interpret predictions. Some questions related to this topic are as follows:

Questions:

Q 1. Is it true that individual trees are independent of each other in bagging trees?

Q2. Which algorithms do not use learning rate as one of their hyperparameters?

  1. Gradient boosting
  2. AdaBoost
  3. Random forest
  4. Extra trees


Q 3. What methodology does the Decision tree take to determine the first split?

  1. Look ahead approach
  2. Brute force approach
  3. Greedy approach
  4. None of these

Q 4. What do you mean by the inductive bias of decision trees?


Q 5. What are some algorithms that you can use for deriving decision trees?


Q 6. Why information gain is given more priority than accuracy while splitting?

  1. Decision tree overfits and accuracy doesn’t help in generalization.
  2. Information gain gives impactful features near the root.
  3. Information gain is more stable than accuracy.
  4. All of the above.


Q 7. Is it true that Random forests have a higher variance of predicted results than Boosted trees? Does it depend upon the data?


Q 8. Which algorithms are not an example of the ensemble learning algorithm?

  1. Adaboost
  2. Extra trees
  3. Decision trees
  4. Gradient boosting


Q 9. Which of these is true for the Random Forest algorithm in a model building?

  1. After using Random forest, you will have interpretability.
  2. The number of trees should be as large as possible.



Q 10. Is it possible to separate the positive class from the negative class for any split on X2?

  1. True
  2. False

Machine Learning Algorithms

Machine learning is a crucial component of artificial intelligence, it enables us to create new apps, web services, robots, and other technological elements. The show recommendations that you get on Netflix are all due to machine learning algorithms. Thus, it proves to be an important section for data science exams and interviews.


Now, let’s go through some important questionnaires for machine learning algorithms.


Q 1. What is the basic difference between supervised and unsupervised machine learning?


Q 2. How does a Receiver Operating Characteristic curve work?


Q 3. p→ 0q is not from which clause?

  1. Horn clause
  2. Hack clause
  3. System clause
  4. Structural clause


Q 4. What is the importance of Bayes’ Theorem in machine learning?


Q 5. How do L1 and L2 regularization differ?


Q 6. What makes the “Naive Bayes” naive?


Q 7. How can you choose an algorithm to apply to a data set?


Q 8. Is there any difference between casualty and correlation?


Q 9. In Model based machine learning methods, an iterative process takes place on ML models made up of which parameters?


Q 10. How can you check the Normality of data sets?


Statistics and probability

Statistics form the heart of data science. It acts as a tool for analyzing data sets to get insights from them. Statistics is used to collect, segregate and deploy data sets with different tasks. Probability helps in making insights through predictions and estimates.


Let’s hop on to some important questions related to statistics and probability.


Q 1. Which of the following are not affected by the presence of Outliers in a dataset?

  1. Range
  2. Mean
  3. Inter-quartile range (IQR)
  4. Standard deviation


Q 2. Can diagonal elements be negative in the symmetric covariance matrix?

  1. Yes
  2. No


Q 3. What is the mean square error for g if g is a point estimator of X?


Q 4. Which of the following expression is not true for X and Y two random variables and a, b, c, d real numbers?

  1. Corr(aX+b, cY+d) = ac*Corr(X, Y) for a,c>0
  2. Cov(aX, cY) = ac*Cov(X, Y)
  3. Cov(aX+b, cY+d) = ac*Cov(X, Y)
  4. Cov(X+b, Y+d) = Cov(X, Y)


Q 5. Is it true that two variables will be independent if Pearson’s correlation between the 2 variables is zero?


Q 6. Is it necessary that expectation and variance will also exist if the characteristic function of a random variable exists?


Q 7. Are the confidence level and margin of error inversely proportional in interval estimation?


Q 8. What do you understand by skewness?


Q 9. How do you calculate the p-value?


Q 10. What are the two types of hypotheses? Why do we need to accept or reject them?



Deep learning

Deep learning is a part of Machine learning that goes through data sets many times to yield outputs. The word “Deep” implies the numerous analyses deep learning involves. It follows the human brain to give outputs, like movie recommendations, driverless cars, etc.


Here are some questions for Deep learning:


Q1. Is it true that in an input, convolutional neural networks can perform various types of transformation?


Q2. In a neural network, which of these do similar tasks as a dropout?

  1. Bagging
  2. Boosting
  3. Stacking
  4. None of these


Q3. Which logic function you cannot use by a perceptron having 2 inputs?

  1. OR.
  2. NOR.
  3. XOR.
  4. AND.



Q4. Which of these variants are based on both momentum and adaptive learning?

  1. Adam
  2. Nesterov
  3. Adagrad
  4. RMSprop


Q5. Is it true that increasing the size of the convolutional kernel will increase the performance of a convolutional network?

Q6. Which activation function is zero centered?

  1. Hyperbolic Tangent
  2. Softmax
  3. Rectified Linear unit
  4. None of the above


Q7. Which of these statements are untrue about Radial Function Neural Network?

  1. It uses the radial basis function as an activation function.
  2. It is a resemblance to Recurrent Neural Networks as they have feedback loops.


Q8. Should you prefer Keras over TensorFlow while doing critical intensive research in a field?

Q9.Is it true that feature extraction has to be done manually in both Machine Learning and Deep Learning algorithms?

Q10. What are hyperparameters?

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Conclusion

We hope you liked the questionnaire, it will help you with preparation for interviews and exams. These are some common yet crucial questions that are often asked in interviews and examinations.

Edudata Online brings the most accurate materials for data science lovers. If you have any queries, then make sure to comment and we will get back to you at the earliest.


Be confident, learn, compete and win.

What are some questions that might be asked on an exam for the certification in data science?

"I make chemistry with words and chemicals"Hey! I am Radhika Mishra, a chemistry enthusiast, and a freelance content writer. I love writing and have been working on regular projects with websites, blogs, and startups. I also help entrepreneurs with LinkedIn branding and optimizing their profiles. Thanks for reading!

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What are some questions that might be asked on an exam for the certification in data science?

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"I make chemistry with words and chemicals"Hey! I am Radhika Mishra, a chemistry enthusiast, and a freelance content writer. I love writing and have been working on regular projects with websites, blogs, and startups. I also help entrepreneurs with LinkedIn branding and optimizing their profiles. Thanks for reading!