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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Model deployment and monitoring - Feature engineering and selection - Supervised and unsupervised learning concepts |
| Topic 2: Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework - IBM Watson ecosystem and components |
| Topic 3: Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - Data governance and cataloging - Data asset management - AutoAI and automatic model building |
| Topic 4: Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Watson Discovery overview - Monitoring deployed models - Watson Assistant integration |
| Topic 5: Data Visualization and Storytelling | 15-20% | - Communicating findings to stakeholders - Visualization best practices - Interactive dashboards and reports |
IBM Watson Data Scientist v1 Sample Questions:
1. Key metrics for a solution should be defined based on:
A) The specific objectives and desired outcomes of the project
B) The most recent technological trends
C) The personal preferences of the project stakeholders
D) The number of available data scientists
2. Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?
A) Filter
B) Order
C) Condition
D) Key
3. Given the Confusion matrix below, which is the formula for specificity?
A) TP/(FN + TP)
B) (TP + TN)/(FN + FP + TN + TP)
C) TP/(FP + TP)
D) TN/(TN + FP)
4. 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) Stratified split
B) Random split without considering the target variable
C) Splitting based on the order of data collection
D) Using only the majority class for splitting
5. Which of the following is a common issue identified during the preprocessing of data?
A) Aesthetically unpleasing charts
B) Excessively large file names
C) Overly detailed documentation
D) Presence of missing values
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: D |
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