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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

Question 1

Which statement describes bagging?

A. Building models and using their output as features into a final model.
B. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
C. Building models in parallel and aggregating their predictions to select the final prediction.
D. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.

Question 2

In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:

A. Testing hypotheses with the smallest investment of time and resources
B. Extending the timeline of the project indefinitely
C. Waiting for all possible features to be developed before release
D. Maximizing the budget before the product launch

Question 3

When managing data with Cloud Pak for Data Services, what is a common task?

A. Reading data from and writing data to Watson Studio
B. Avoiding the automation of data processing tasks
C. Using the same data management approach for all types of data, regardless of size or format
D. Ignoring data security and compliance requirements

Question 4

Given the Confusion matrix below, which is the formula for specificity?

A. (TP + TN)/(FN + FP + TN + TP)
B. TN/(TN + FP)
C. TP/(FN + TP)
D. TP/(FP + TP)

Question 5

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

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

Solutions: