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IBM C1000-154 Exam : IBM Watson Data Scientist v1
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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| Topic 2: Prepare the Data | 18% | - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Feature engineering and selection - Handle missing values and outliers |
| Topic 3: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 4: Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Select appropriate ML algorithms - Perform hyperparameter tuning - Compare and select best performing models |
| Topic 5: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 6: Collect and Explore the Data | 15% | - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio - Detect patterns, outliers, and correlations |
| Topic 7: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Ensure scalability and reliability - Monitor model performance post-deployment |
| Topic 8: Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
IBM Watson Data Scientist v1 Sample Questions:
Assessing the feasibility of a solution(s) often requires evaluating:
- A. Market competition only
- B. Preferred communication channels of the project manager
- C. The color scheme of the user interface
- D. Technical feasibility, cost, and time constraints
Correct Answer: D 🗳️
Which of the following is a critical first step in understanding a business problem for data science projects?
- A. Selecting the machine learning algorithm
- B. Deploying the model
- C. Choosing the visualization tools
- D. Defining the project scope
Correct Answer: D 🗳️
F1-score is particularly useful when:
- A. You need a balance between precision and recall.
- B. The dataset size is extremely large.
- C. The data is completely balanced.
- D. Only the model's accuracy matters.
Correct Answer: A 🗳️
Given the Confusion matrix below, which is the formula for specificity?
- A. (TP + TN)/(FN + FP + TN + TP)
- B. TN/(TN + FP)
- C. TP/(FN + TP)
- D. TP/(FP + TP)
Correct Answer: B 🗳️
Which statement describes bagging?
- A. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
- B. Building models and using their output as features into a final model.
- C. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
- D. Building models in parallel and aggregating their predictions to select the final prediction.
Correct Answer: D 🗳️
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