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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| 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: Prepare the Data | 18% | - Feature engineering and selection - Handle missing values and outliers - Use Watson tools for data preparation - Clean, transform, and normalize datasets |
| Topic 5: Build the Model | 20% | - Select appropriate ML algorithms - Compare and select best performing models - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning |
| Topic 6: Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| Topic 7: Collect and Explore the Data | 15% | - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations - Identify and access data sources in Watson Studio |
| Topic 8: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Monitor model performance post-deployment - Ensure scalability and reliability |
IBM Watson Data Scientist v1 Sample Questions:
1. What is a benefit of creating data pipelines to automate the model lifecycle?
A) It provides a structured approach to processing, validating, and deploying models
B) Encourages a one-size-fits-all approach to model development
C) Reduces the need for understanding the underlying data
D) It necessitates frequent manual updates and checks
2. Choosing the best model often involves trade-offs.
Which scenario represents such a trade-off?
A) Choosing the model with the largest number of features, regardless of performance
B) Selecting a model based solely on its execution speed, without regard to accuracy
C) Opting for the most complex model to ensure ease of use
D) Preferring a model with higher accuracy over one that is slightly less accurate but much more interpretable
3. Which feature is NOT available when managing models with Watson Machine Learning?
A) Real-time performance monitoring
B) Automatic conversion of all models to deep learning models
C) Version control of deployed models
D) Rollback capabilities for model versions
4. 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 is unable to handle discrete parameters
D) It requires no prior knowledge of the hyperparameters
5. A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions).
Which metric is a good indicator of the performance of the virtual assistant?
A) The Area Under the Curve (AUC)
B) Measure escalated calls using A/B testing
C) The Root Mean Squared Error (RMSE) of words
D) The F1 score of predicted intents in the Analytics tab
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: B |
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