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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| 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 |
| Evaluate the Model | 15% | - Assess classification/regression metrics - Validate model generalizability - Identify bias and overfitting |
| Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Prepare the Data | 18% | - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation - Feature engineering and selection |
| Build the Model | 20% | - Perform hyperparameter tuning - Compare and select best performing models - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
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) Utilizing pandas to clean and transform data
B) Only analyzing datasets with less than 100 rows
C) Avoiding the use of visualizations to understand data
D) Ignoring missing values in the dataset
2. Key metrics for a solution should be defined based on:
A) The personal preferences of the project stakeholders
B) The number of available data scientists
C) The most recent technological trends
D) The specific objectives and desired outcomes of the project
3. What is the key difference between batch processing and streaming in data processing?
A) Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives
B) Batch processing involves real-time data processing, whereas streaming does not process data
C) Streaming is suitable for large, historical datasets, whereas batch processing is for real-time data analysis
D) Batch processing processes data in large blocks at a time, whereas streaming processes data in real- time as it arrives
4. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?
A) Accuracy
B) Recall
C) Precision
D) F1-score
5. What is the primary purpose of partitioning data into training and test sets?
A) To evaluate the model's performance on unseen data
B) To ensure that the model gets exposed to all possible data scenarios during training
C) To increase the computational efficiency of model training
D) To maximize the accuracy of the model by using all data for training
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A |
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