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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?

A) Decision Trees
B) Support Vector Machines
C) Linear Regression
D) K-Means Clustering

2. How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?

A) Business Understanding
B) Data Preparation
C) Evaluation
D) Model Building

3. Which metric is commonly used to evaluate the performance of a regression model?

A) Accuracy
B) Recall
C) Mean Squared Error (MSE)
D) Precision

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

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

5. Cloud Pak for Data's integration with Spark allows users to:

A) Leverage distributed computing for processing large datasets efficiently
B) Perform complex computations on small datasets only
C) Avoid using any form of data processing or analysis
D) Use Spark exclusively for data visualization purposes

Solutions: