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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

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

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

2. How can data splits be made reproducible in a machine learning experiment?

A) By splitting the data in a sequential manner without randomization
B) By using a different random seed each time the data is split
C) By using a consistent random seed when splitting the data
D) By partitioning the data manually

3. When helping businesses articulate and define problems, what is an essential first step?

A) Selecting the analytical techniques
B) Defining key performance indicators (KPIs)
C) Establishing a clear problem statement
D) Identifying potential data sources

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

5. What does the term "complexity" in model comparison refer to?

A) The size of the dataset the model can handle
B) The number of hyperparameters that need to be tuned
C) The amount of computational resources required for training and inference
D) The aesthetic appeal of the model's graphical representations

Solutions: