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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Project management and collaboration - Data asset management - Data governance and cataloging |
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components |
| Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Watson Discovery overview - Monitoring deployed models - Watson Assistant integration |
| Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Feature engineering and selection - Model deployment and monitoring - Model training, evaluation, and optimization |
| Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
IBM Watson Data Scientist v1 Sample Questions:
Question 1
Which of the following is a critical first step in understanding a business problem for data science projects?
A. Deploying the model
B. Choosing the visualization tools
C. Defining the project scope
D. Selecting the machine learning algorithm
Question 2
Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A. Pandas
B. PyTorch
C. Keras
D. TensorFlow
Question 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. The F1 score of predicted intents in the Analytics tab
B. The Root Mean Squared Error (RMSE) of words
C. Measure escalated calls using A/B testing
D. The Area Under the Curve (AUC)
Question 4
In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
A. Choosing the most aesthetically pleasing user interface
B. Selecting the programming language with the least number of keywords
C. Determining the computational power and memory requirements for the deployed solution
D. Focusing exclusively on the cost of storage
Question 5
Which statement describes bagging?
A. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
B. Building models and using their output as features into a final model.
C. Building models in parallel and aggregating their predictions to select the final prediction.
D. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
Solutions:
| Question 1 Answer: C | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: C | Question 5 Answer: C |
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