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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis |
| Topic 2: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 3: Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
| Topic 4: Build the Model | 20% | - Perform hyperparameter tuning - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS - Compare and select best performing models |
| Topic 5: Evaluate the Model | 15% | - Validate model generalizability - Identify bias and overfitting - Assess classification/regression metrics |
| Topic 6: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 7: Deploy the Solution | 10% | - Monitor model performance post-deployment - Deploy models as APIs in Watson - Ensure scalability and reliability |
| Topic 8: Prepare the Data | 18% | - Feature engineering and selection - Handle missing values and outliers - Use Watson tools for data preparation - Clean, transform, and normalize datasets |
IBM Watson Data Scientist v1 Sample Questions:
Question 1
When would you use AutoAI to select algorithms for your model?
A. When the model requirements are extremely specific and no standard algorithm fits.
B. When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
C. Only when working with small datasets due to processing limitations.
D. When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
Question 2
In classification models, which of the following metrics is NOT directly derived from the confusion matrix?
A. F1-score
B. Mean Absolute Error (MAE)
C. Recall
D. Precision
Question 3
What is data leakage in the context of model training?
A. Leakage of sensitive information due to poor data handling practices
B. Loss of data during the splitting process
C. When data from outside the training dataset is accidentally included in the training process
D. A situation where the test data is not available
Question 4
How can data splits be made reproducible in a machine learning experiment?
A. By splitting the data in a sequential manner without randomization
B. By using a different random seed each time the data is split
C. By using a consistent random seed when splitting the data
D. By partitioning the data manually
Question 5
In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
A. It focuses on maintaining and operating solutions at scale.
B. It involves the rapid building of prototypes to validate ideas.
C. It is primarily concerned with the technical deployment of solutions.
D. It emphasizes understanding user outcomes and business needs.
Solutions:
| Question 1 Answer: D | Question 2 Answer: B | Question 3 Answer: C | Question 4 Answer: C | Question 5 Answer: D |
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