IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare the Data | 18% | - Feature engineering and selection - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation |
| Topic 2: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Ensure scalability and reliability - Monitor model performance post-deployment |
| Topic 3: Build the Model | 20% | - Perform hyperparameter tuning - Train models using Watson AutoAI and SPSS - Select appropriate ML algorithms - Compare and select best performing models |
| Topic 4: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 5: Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| Topic 6: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 7: Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis |
| Topic 8: Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
IBM Watson Data Scientist v1 Sample Questions:
1. When using pandas in a Jupyter notebook for exploratory data analysis, what is a common practice?
A) Ignoring missing values in the dataset
B) Avoiding the use of visualizations to understand data
C) Only analyzing datasets with less than 100 rows
D) Utilizing pandas to clean and transform data
2. What is a key disadvantage of using Grid Search for hyperparameter tuning?
A) It is unable to handle discrete parameters
B) It requires no prior knowledge of the hyperparameters
C) It is too quick and may miss out on evaluating some hyperparameters
D) It can be computationally expensive and time-consuming due to its exhaustive nature
3. Assessing the feasibility of a solution(s) often requires evaluating:
A) The color scheme of the user interface
B) Preferred communication channels of the project manager
C) Market competition only
D) Technical feasibility, cost, and time constraints
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) Using only the majority class for splitting
C) Splitting based on the order of data collection
D) Random split without considering the target variable
5. Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
A) Grid Search
B) Random Search
C) Sequential Search
D) Binary Search
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: A |
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