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Free PDF IBM - Authoritative C1000-154 - IBM Watson Data Scientist v1 Latest Braindumps Pdf

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IBM Watson Data Scientist v1 certification exam is designed to test the candidates' knowledge and skills in different areas such as data acquisition, data preparation, exploratory data analysis, feature engineering, model selection, and evaluation. C1000-154 Exam compliments the IBM Watson Studio platform to provide an integrated environment to build, train and deploy machine learning models. C1000-154 exam helps you to validate your expertise in working with big data and machine learning tools, which will enable you to contribute to the implementation of forward-thinking data solutions.

IBM is a global leader when it comes to technology solutions and innovation. IBM has always adapted to industry changes and constantly upgraded itself to provide advanced solutions to its customers. The IBM Watson Data Scientist v1 is a credential that recognizes professionals who have the ability to use IBM Watson tools and technologies to analyze data, build predictive models, and deploy AI solutions.

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IBM Watson Data Scientist v1 Sample Questions (Q50-Q55):

NEW QUESTION # 50
Which feature is NOT available when managing models with Watson Machine Learning?

A. Real-time performance monitoring
B. Rollback capabilities for model versions
C. Automatic conversion of all models to deep learning models
D. Version control of deployed models

Answer: C

NEW QUESTION # 51
Which analytic technique is NOT typically used to address business requirements?

A. Proofreading
B. Decision trees
C. Clustering
D. Regression analysis

Answer: A

NEW QUESTION # 52
What is a key disadvantage of using Grid Search for hyperparameter tuning?

A. It is too quick and may miss out on evaluating some hyperparameters
B. It is unable to handle discrete parameters
C. It can be computationally expensive and time-consuming due to its exhaustive nature
D. It requires no prior knowledge of the hyperparameters

Answer: C

NEW QUESTION # 53
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. Splitting based on the order of data collection
B. Stratified split
C. Using only the majority class for splitting
D. Random split without considering the target variable

Answer: B

NEW QUESTION # 54
In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?

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

Answer: A

NEW QUESTION # 55
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