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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework |
| Watson AI Services and Deployment | 10-15% | - Watson Discovery overview - Watson Assistant integration - Deploying models as REST APIs - Monitoring deployed models |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Data asset management - Project management and collaboration - Data governance and cataloging |
| Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Model training, evaluation, and optimization - Feature engineering and selection - Model deployment and monitoring |
IBM Watson Data Scientist v1 Sample Questions:
Question 1
What is the primary purpose of partitioning data into training and test sets?
A. To evaluate the model's performance on unseen data
B. To ensure that the model gets exposed to all possible data scenarios during training
C. To increase the computational efficiency of model training
D. To maximize the accuracy of the model by using all data for training
Question 2
Profiling and visualizing data using Watson tools primarily helps in:
A. Creating aesthetically pleasing presentations without regard to data relevance
B. Simplifying the data collection process without analyzing quality
C. Increasing the quantity of data for analysis
D. Identifying patterns, outliers, and insights in the data
Question 3
Which hyperparameter is NOT commonly adjusted in a deep learning model?
A. Number of layers
B. The color of the model's output
C. Activation function
D. Learning rate
Question 4
Which analytic technique is NOT typically used to address business requirements?
A. Clustering
B. Proofreading
C. Decision trees
D. Regression analysis
Question 5
Selecting the right model for a data science project depends on:
A. The preference of the data scientist
B. The size of the dataset only
C. The project's budget only
D. The type of data and the problem to be solved
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: B | Question 4 Answer: B | Question 5 Answer: D |
Dale 
Cora 
Larry 
Luther 
Sibyl 
Boyce 
Gerald 
Pearl 
Amy 
Dylan 
Venus 
Yehudi 
Nicola 
Gail 
