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Last Updated: Sep 01, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Visualization best practices - Communicating findings to stakeholders |
| Topic 2: Machine Learning and Model Development | 20-25% | - Feature engineering and selection - Model training, evaluation, and optimization - Model deployment and monitoring - Supervised and unsupervised learning concepts |
| Topic 3: Watson Studio and Watson Knowledge Catalog | 20-25% | - AutoAI and automatic model building - Project management and collaboration - Data asset management - Data governance and cataloging |
| Topic 4: Data Science and Watson Fundamentals | 20-25% | - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration - IBM Watson ecosystem and components |
| Topic 5: Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Watson Discovery overview - Watson Assistant integration - Deploying models as REST APIs |
Question 1
Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?
A. conda
B. vim
C. pip
D. sudo
E. bash
Question 2
An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?
A. Data Preparation
B. Data Understanding
C. Business Understanding
D. Modeling
Question 3
What is the primary purpose of hyperparameter tuning in machine learning models?
A. To increase the number of features in the dataset automatically
B. To ensure the model uses all available computational resources
C. To reduce the training time of the model to an absolute minimum
D. To adjust the model's complexity to improve its performance on unseen data
Question 4
In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?
A. Decision Trees
B. K-Means Clustering
C. Linear Regression
D. Support Vector Machines
Question 5
What is a key disadvantage of using Grid Search for hyperparameter tuning?
A. It can be computationally expensive and time-consuming due to its exhaustive nature
B. It is too quick and may miss out on evaluating some hyperparameters
C. It requires no prior knowledge of the hyperparameters
D. It is unable to handle discrete parameters
Solutions:
| Question 1 Answer: A,C | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: B | Question 5 Answer: A |
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