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While making revisions and modifications to the IBM C1000-154 practice exam, our team takes reports from over 90,000 professionals worldwide to make the IBM Watson Data Scientist v1 exam questions foolproof. To make you capable of preparing for the IBM C1000-154 Exam smoothly, we provide actual IBM C1000-154 exam dumps.

IBM Watson Data Scientist v1 certification exam consists of 60 multiple-choice questions that must be completed within 90 minutes. C1000-154 exam covers a range of topics, including data exploration, data preparation, model development, model evaluation, and deployment. It also covers machine learning algorithms, deep learning, and natural language processing. C1000-154 Exam is available in English and Japanese.

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IBM Watson Data Scientist certification exam is a challenging test that requires a deep understanding of data science concepts and techniques. To pass the exam, you will need to demonstrate your ability to analyze data sets, develop predictive models, and use machine learning algorithms. You will also need to be familiar with IBM Watson tools like Watson Studio, Watson Knowledge Catalog, and Watson Machine Learning.

IBM Watson Data Scientist v1 Sample Questions (Q15-Q20):

NEW QUESTION # 15
Assessing the feasibility of a solution(s) often requires evaluating:

A. Market competition only
B. The color scheme of the user interface
C. Preferred communication channels of the project manager
D. Technical feasibility, cost, and time constraints

Answer: D

NEW QUESTION # 16
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. The F1 score of predicted intents in the Analytics tab
B. Measure escalated calls using A/B testing
C. The Root Mean Squared Error (RMSE) of words
D. The Area Under the Curve (AUC)

Answer: B

NEW QUESTION # 17
What is a critical consideration when selecting the right model class for a given problem?

A. The nature of the problem (e.g., classification, regression) and the characteristics of the data.
B. The theoretical complexity of the model, with more complex models always being preferred.
C. The availability of high-performance computing resources.
D. The model's ability to produce results quickly, regardless of accuracy.

Answer: A

NEW QUESTION # 18
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. Using only the majority class for splitting
B. Stratified split
C. Random split without considering the target variable
D. Splitting based on the order of data collection

Answer: B

NEW QUESTION # 19
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. Choosing the visualization tools
D. Deploying the model

Answer: B

NEW QUESTION # 20
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