IBM Watson Data Scientist v1 : C1000-154

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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. Which statement describes bagging?

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

2. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?

A) Accuracy
B) F1-score
C) Recall
D) Precision

3. A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions).
Which metric is a good indicator of the performance of the virtual assistant?

A) Measure escalated calls using A/B testing
B) The Root Mean Squared Error (RMSE) of words
C) The Area Under the Curve (AUC)
D) The F1 score of predicted intents in the Analytics tab

4. In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?

A) Random split without considering the target variable
B) Splitting based on the order of data collection
C) Using only the majority class for splitting
D) Stratified split

5. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?

A) sudo
B) conda
C) pip
D) vim
E) bash

Solutions: