data science (1)

DATA SCIENCE

Exam Instructions:

Total Number of Questions: 45
You will be required to answer a total of 45 multiple-choice questions.

Time Limit: 15 minutes for the entire exam
 Once the time is up, the exam will automatically submit.

Passing Criteria:
A minimum score of 50% is required to pass the exam

Multiple Attempts:
You are allowed to take the exam multiple times. Only your highest score will be considered for certification.

Additional Instructions:

The exam timer cannot be paused once it begins.

Good luck, and feel free to retake the exam to improve your score!


data science

DATA SCIENCE

Test your DATA SCIENCE skills with a challenging exam designed to evaluate your knowledge in programming, data structures, and algorithms

The certificate will be generated based on the information you provide in the form, so please ensure that all details are entered correctly.

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1) What is ‘Data Mining’ primarily concerned with?

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2) What does the term ‘gradient descent’ refer to?

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3) What is the primary focus of ‘Time Series Forecasting’?

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4) What does ‘KNN’ stand for in machine learning?

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5) What is the main advantage of using ‘XGBoost’?

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6) Which of the following is a technique for ‘Feature Selection’?

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7) What does ‘Dimensionality Curse’ refer to?

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8) What is the aim of ‘Text Mining’?

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9) What is the main benefit of ‘k-fold cross-validation’?

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10) What is the purpose of the confusion matrix?

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11) What is the significance of ‘time series forecasting’?

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12) Which of the following is a characteristic of ‘Deep Learning’?

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13) What does ‘T-test’ compare in statistics?

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14) What is the primary goal of ‘A/B testing’?

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15) What does ‘data pipeline’ refer to in data engineering?

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16) Which of the following best describes ‘recurrent neural networks’ (RNN)?

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17) What does the term ‘loss function’ refer to in machine learning?

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18) What does the term ‘feature vector’ refer to?

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19) Which of the following is a disadvantage of ‘Decision Trees’?

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20) Which of the following is a disadvantage of ‘Neural Networks’?

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21) Which of the following is a supervised learning technique?

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22) What does ‘Dimensionality Reduction’ achieve in machine learning?

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23) What does ‘Time Series Analysis’ help to analyze?

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24) What is ‘tuning’ in the context of neural networks?

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25) What is the purpose of data visualization?

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26) What is the purpose of ‘Data Sampling’?

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27) What does ‘LSTM’ stand for in deep learning?

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28) What does the term ‘confidence interval’ indicate?

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29) What does ‘Bayesian Inference’ allow us to update?

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30) Which of the following is a common clustering algorithm?

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31) What is the purpose of ‘Data Augmentation’ in deep learning?

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32) What does ‘Data Leakage’ refer to?

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33) What does ‘ROC Curve’ represent in classification models?

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34) Which type of learning is supervised learning?

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35) What is the purpose of ‘Feature Scaling’?

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36) What does ‘Confusion Matrix’ represent in classification?

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37) Which of the following is a common data visualization tool?

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38) Which algorithm is commonly used for natural language processing?

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39) What is ‘Natural Language Processing’ primarily used for?

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40) What does the term ‘natural language generation’ refer to?

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41) What does the term ‘training loss’ refer to in neural networks?

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42) What does the term ‘precision’ refer to in classification tasks?

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43) Which of the following is an example of a continuous variable?

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44) Which of the following is NOT a characteristic of decision trees?

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45) What does ‘Bagging’ help to improve in ensemble methods?

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