Skip to main content

C1000-059 Real Exam Questions

IBM AI Enterprise Workflow V1 Data Science Specialist

62 questions available · Page 1 of 7

Updated Exam DumpsVerified AnswersPass Guarantee

Get Complete Exam Dumps
Question 1 Single choice

Which statement is true for naive Bayes?

  1. A

    Naive Bayes can be used for regression.

  2. B

    Let p(C1 | x) and p(C2 | x) be the conditional probabilities that x belongs to class C1 and C2

    respectively, in a binary model, log p (C1 | x) - log p(C2 | x) > 0 results in predicting that x belongs to
    C2.

  3. C

    Naive Bayes is a conditional probability model.

  4. D

    Naive Bayes doesn't require any assumptions about the distribution of values associated with each

    class.

Show answer and explanation

Correct answer: C

Explanation

References:
http://users.sussex.ac.uk/~christ/crs/ml/lec02b.html

Question 2 Single choice

Which is a preferred approach for simplifying the data transformation steps in machine learning model management and maintenance?

  1. A

    Implement data transformation, feature extraction, feature engineering, and imputation algorithms in

    one single pipeline.

  2. B

    Do not apply any data transformation or feature extraction or feature engineering steps.

  3. C

    Leverage only deep learning algorithms.

  4. D

    Apply a limited number of data transformation steps from a pre-defined catalog of possible operations

    independent of the machine learning use case.

Show answer and explanation

Correct answer: B

Question 3 Multiple choice

What are two methods used to detect outliers in structured data? (Choose two.)

  1. A

    multi-label classification

  2. B

    isolation forest

  3. C

    gradient descent

  4. D

    one class Support Vector Machine (SVM)

  5. E

    Word2Vec

Show answer and explanation

Correct answers: B, D

Explanation

References:
https://www.researchgate.net/post/What-is-the-best-outliers-detection-algorithm-to-used-for-big-data

Question 4 Single choice

What is used to scale large positive values during data cleaning?

  1. A

    division by random numbers

  2. B

    square

  3. C

    logarithm

  4. D

    subtract median

Show answer and explanation

Correct answer: A

Question 5 Single choice

Determine the number of bigrams and trigrams in the sentence.

"Data is the new oil".

  1. A

    3 bigrams, 3 trigrams

  2. B

    4 bigrams, 4 trigrams

  3. C

    3 bigrams, 4 trigrams

  4. D

    4 bigrams, 3 trigrams

Show answer and explanation

Correct answer: A

Question 6 Single choice

What is a class of machine learning problems where the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs?

  1. A

    unsupervised learning

  2. B

    mentoring

  3. C

    reinforcement learning

  4. D

    supervised learning

Show answer and explanation

Correct answer: D

Explanation

References:
https://en.wikipedia.org/wiki/Machine_learning

Question 7 Single choice

In machine vision, the algorithm for detecting objects or features in an image based on a target pattern is known as?

  1. A

    OCR

  2. B

    Hough transformation

  3. C

    Fourier transform

  4. D

    normalized correlation

Show answer and explanation

Correct answer: D

Question 8 Single choice

Which measure can be used to show business stakeholders the likelihood that a machine learning model will generate a true prediction?

  1. A

    accuracy

  2. B

    variance

  3. C

    mean

  4. D

    skewness

Show answer and explanation

Correct answer: A

Question 9 Single choice

The least squares optimization technique (The Method of Least Squares) is used in which algorithm?

  1. A

    Support Vector Machines

  2. B

    Naive Bayes classification

  3. C

    Logistic regression

  4. D

    Linear regression

Show answer and explanation

Correct answer: D

Explanation

References:
https://arxiv.org/ftp/arxiv/papers/1804/1804.05665.pdf

Question 10 Single choice

Which statement is true in the context of evaluating metrics for machine learning algorithms?

  1. A

    A random classifier has AUC (the area under ROC curve) of 0.5

  2. B

    Using only one evaluation metric is sufficient

  3. C

    The F-score is always equal to precision

  4. D

    Recall of 1 (100%) is always a good result

Show answer and explanation

Correct answer: B