IBM C1000-154 Exam : IBM Watson Data Scientist v1

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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. When deploying models in Watson Machine Learning, what is essential for ensuring the models perform as expected in production?

A) Continuous monitoring and evaluation of model performance
B) Limiting access to the model to a few select users
C) Deployment without any security measures
D) Using the highest number of resources for every model

2. Which of the following is a critical first step in understanding a business problem for data science projects?

A) Selecting the machine learning algorithm
B) Defining the project scope
C) Deploying the model
D) Choosing the visualization tools

3. What is the primary purpose of partitioning data into training and test sets?

A) To maximize the accuracy of the model by using all data for training
B) To increase the computational efficiency of model training
C) To evaluate the model's performance on unseen data
D) To ensure that the model gets exposed to all possible data scenarios during training

4. Profiling and visualizing data using Watson tools primarily helps in:

A) Simplifying the data collection process without analyzing quality
B) Creating aesthetically pleasing presentations without regard to data relevance
C) Identifying patterns, outliers, and insights in the data
D) Increasing the quantity of data for analysis

5. When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?

A) Compatibility of the algorithm with the data characteristics and the predictive task.
B) The popularity of the algorithm in recent academic papers.
C) Choosing algorithms that are only based on supervised learning.
D) The algorithm that requires the least amount of data preprocessing.

Solutions: