IBM C1000-059 Online Practice Questions and Exam Preparation

IBM C1000-059 Online Questions & Answers

• Question 1:

  What is the technique called for vectorizing text data which matches the words in different sentences to determine if the sentences are similar?

  A. Cup of Vectors
  B. Box of Lexicon
  C. Sack of Sentences
  D. Bag of Words
  D. Bag of Words

• Question 2:

  A new test to diagnose a disease is evaluated on 1152 people, and 106 people have the disease, and 1046 people do not have the disease. The test results are summarized below:

  

  In this sample, how many cases are false positives and false negatives?

  A. 33 false positives and 81 false negatives
  B. 81 false positives and 73 false negatives
  C. 73 false positives and 81 false negatives
  D. 81 false positives and 33 false negatives
  A. 33 false positives and 81 false negatives

• Question 3:

  Which test is applied to determine the relationship between two categorical variables?

  A. paired t-test
  B. chi squared test
  C. z test
  D. t-test
  B. chi squared test

• Question 4:

  Determine the number of bigrams and trigrams in the sentence. "Data is the new oil".

  A. 3 bigrams, 3 trigrams
  B. 4 bigrams, 4 trigrams
  C. 3 bigrams, 4 trigrams
  D. 4 bigrams, 3 trigrams
  A. 3 bigrams, 3 trigrams

• Question 5:

  Which situation would disqualify a machine learning system from being used for a particular use case?

  A. The use case requires a 100% likelihood of making a correct/true prediction.
  B. Training and testing data for the model contain outliers.
  C. Data for the machine learning model is available only as static CSV files.
  D. The neural network for the model requires significantly more computing power than a logistic regression model.
  D. The neural network for the model requires significantly more computing power than a logistic regression model.

• Question 6:

  DRAG DROP

  What is the best step by step order for machine learning pipeline?

  Select and Place:

  

  

• Question 7:

  What is the goal of the backpropagation algorithm?

  A. to randomize the trajectory of the neural network parameters during training
  B. to smooth the gradient of the loss function in order to avoid getting trapped in small local minimas
  C. to scale the gradient descent step in proportion to the gradient magnitude
  D. to compute the gradient of the loss function with respect to the neural network parameters
  B. to smooth the gradient of the loss function in order to avoid getting trapped in small local minimas

• Question 8:

  Which statement defines p-value?

  A. It is the probability of accepting a null hypothesis when the hypothesis is proven true.
  B. It is the probability of rejecting a null hypothesis when the hypothesis is proven false.
  C. It is the probability of accepting a null hypothesis when the hypothesis is proven false.
  D. It is the probability of rejecting a null hypothesis when the hypothesis is proven true.
  C. It is the probability of accepting a null hypothesis when the hypothesis is proven false.

• Question 9:

  Which is an example of a nominal scale data?

  A. a variable industry with categorical values such as financial, engineering, and retail
  B. a variable mood with a scale of values unhappy, ok, and happy
  C. a variable bank account balance whose possible values are $5, $10, and $15
  D. a variable temperature with a scale of values low, medium, and high
  C. a variable bank account balance whose possible values are $5, $10, and $15

• Question 10:

  Given the following matrix multiplication:

  

  What is the value of P?

  A. ?
  B. 17
  C. 12
  D. ?
  C. 12