IBM C1000-059 Online Practice Questions and Exam Preparation

IBM C1000-059 Online Questions & Answers

• Question 1:

  Which is the most important thing to ensure while collecting data?

  A. samples collected are skewed with each other
  B. samples collected are all strongly correlated with each other
  C. samples collected adequately cover the space of all possible scenarios
  D. samples collected focus only on the most common cases
  A. samples collected are skewed with each other

• Question 2:

  What is the meaning of "deep" in deep learning?

  A. To go deep into the loss function landscape.
  B. The higher the number of machine learning algorithms that can be applied, the deeper is the learning.
  C. A kind of deeper understanding achieved by any approach taken.
  D. It indicates the many layers contributing to a model of the data.
  D. It indicates the many layers contributing to a model of the data.

• Question 3:

  Which algorithm is best suited if a client needs full explainability of the machine learning model?

  A. decision tree
  B. logistic regression
  C. support vector machine (SVM)
  D. recurrent neural network
  A. decision tree

• Question 4:

  Given the following sentence:

  The dog jumps over a fence.

  What would a vectorized version after common English stopword removal look like?

  A. ['dog', 'fence', 'run']
  B. ['fence', 'jumps']
  C. ['dog', 'fence', 'jumps']
  D. ['a', 'dog', 'fence', 'jumps', 'over', 'the']
  C. ['dog', 'fence', 'jumps']

• Question 5:

  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 6:

  What is the primary role of a data steward?

  A. they are a "blue sky thinker" who comes up with new approaches to use new data in innovative ways
  B. they have a strong understanding of the enterprise's database architecture
  C. they define data processes to meet compliance and regulatory obligations
  D. the one who collects, processes, and performs statistical analysis on data
  D. the one who collects, processes, and performs statistical analysis on data

• Question 7:

  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 8:

  A data scientist is exploring transaction data from a chain of stores with several locations. The data includes store number, date of sale, and purchase amount.

  If the data scientist wants to compare total monthly sales between stores, which two options would be good ways to aggregate the data? (Choose two.)

  A. Find the sum of the transaction prices
  B. Select the largest transaction amount by month and store
  C. Write a GROUP BY query
  D. Plot a time series plot of transaction amounts
  E. Generate a pivot table
  B. Select the largest transaction amount by month and store
  D. Plot a time series plot of transaction amounts

• Question 9:

  A data analyst creates a term-document matrix for the following sentence:

  I saw a cat, a dog and another cat.

  Assuming they used a binary vectorizer, what is the resulting weight for the word cat?

  B. 1
  C. 3
  D. 2
  B. 1

• Question 10:

  In a hyperparameter search, whether a single model is trained or a lot of models are trained in parallel is largely determined by?

  A. The number of hyperparameters you have to tune.
  B. The presence of local minima in your neural network.
  C. The amount of computational power you can access.
  D. Whether you use batch or mini-batch optimization.
  C. The amount of computational power you can access.