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IBM C1000-059 Practice Test Questions Answers

Exam Code: C1000-059 (Updated 62 Q&As with Explanation)
Exam Name: IBM AI Enterprise Workflow V1 Data Science Specialist
Last Update: 10-Aug-2025
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Questions Include:

  • Single Choice: 48 Q&A's
  • Multiple Choice: 13 Q&A's
  • Drag Drop: 1 Q&A's

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    C1000-059 Questions and Answers

    Question # 1

    What are three operators used by genetic programming? (Choose three.)

    A.

    reciprocation

    B.

    mutation

    C.

    duel

    D.

    selection

    E.

    sheltering

    F.

    crossover

    Question # 2

    What are two key characteristics of cloud architecture that could benefit AI applications? (Choose two.)

    A.

    constant attention needed for maintenance and support of the cloud platform

    B.

    capable of managing and handling dynamic workloads with automatic recovery from failures

    C.

    hybrid clouds enable the deployment of distributed large neural networks

    D.

    support for common business oriented language (COBOL) applications

    E.

    the hardware requirement can be scaled up as per the demand

    Question # 3

    Which fine-tuning technique does not optimize the hyperparameters of a machine learning model?

    A.

    grid search

    B.

    population based training

    C.

    random search

    D.

    hyperband

    Question # 4

    What are three elements that are typically part of a machine learning pipeline in scikit-learn or pyspark? (Choose three.)

    A.

    model building

    B.

    data preprocessing

    C.

    model prediction

    D.

    business understanding

    E.

    use case selection

    F.

    data exploration

    Question # 5

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

    A.

    Implement data transformation, feature extraction, feature engineering, and imputation algorithms in one single pipeline.

    B.

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

    C.

    Leverage only deep learning algorithms.

    D.

    Apply a limited number of data transformation steps from a pre-defined catalog of possible operations independent of the machine learning use case.

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