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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Monitor model performance post-deployment - Ensure scalability and reliability |
| Topic 2: Evaluate the Model | 15% | - Assess classification/regression metrics - Validate model generalizability - Identify bias and overfitting |
| Topic 3: Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations |
| Topic 4: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 5: Understand the Business Problem | 12% | - Define success metrics and constraints - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) |
| Topic 6: Prepare the Data | 18% | - Use Watson tools for data preparation - Handle missing values and outliers - Feature engineering and selection - Clean, transform, and normalize datasets |
| Topic 7: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 8: Build the Model | 20% | - Select appropriate ML algorithms - Compare and select best performing models - Perform hyperparameter tuning - Train models using Watson AutoAI and SPSS |
IBM Watson Data Scientist v1 Sample Questions:
1. Which of the following is true about the AUC measure in the context of classification models?
A) It measures the model's accuracy using a single threshold.
B) It indicates the number of false positives.
C) It represents the degree of separability between classes.
D) It is less useful when the classes are highly imbalanced.
2. In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?
A) Support Vector Machines
B) Decision Trees
C) Linear Regression
D) K-Means Clustering
3. Which two graph types are used in EDA to show the relationship between two or more quantitative variables?
A) Stem-and-leaf plot
B) Scatter plot
C) Box plot
D) Heat map
E) Histogram
4. When anticipating additional data sources that might be relevant, what is a crucial factor to consider?
A) The relevance of the data source to the business problem
B) The graphical interface of the data source
C) The color scheme of the data visualization
D) The data source's popularity on social media
5. What is a benefit of creating data pipelines to automate the model lifecycle?
A) It necessitates frequent manual updates and checks
B) Encourages a one-size-fits-all approach to model development
C) It provides a structured approach to processing, validating, and deploying models
D) Reduces the need for understanding the underlying data
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B,D | Question # 4 Answer: A | Question # 5 Answer: C |
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