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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| Build the Model | 20% | - Perform hyperparameter tuning - Train models using Watson AutoAI and SPSS - Compare and select best performing models - Select appropriate ML algorithms |
| 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 |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Deploy the Solution | 10% | - Deploy models as APIs in Watson - Ensure scalability and reliability - Monitor model performance post-deployment |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Understand the Business Problem | 12% | - Define success metrics and constraints - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) |
| Prepare the Data | 18% | - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation - Feature engineering and selection |
IBM Watson Data Scientist v1 Sample Questions:
1. Which two graph types are used in EDA to show the relationship between two or more quantitative variables?
A) Box plot
B) Heat map
C) Histogram
D) Stem-and-leaf plot
E) Scatter plot
2. In Cognos Analytics, which two features distinguish stories from dashboards?
A) Stories automatically load different filters for different users.
B) Stories are text-based and do not contain visualizations.
C) Stories convey a conclusion.
D) Stories provide a narrative over time.
E) Stories can be embedded in websites or documents.
3. What is data leakage in the context of model training?
A) A situation where the test data is not available
B) Leakage of sensitive information due to poor data handling practices
C) When data from outside the training dataset is accidentally included in the training process
D) Loss of data during the splitting process
4. What is a key disadvantage of using Grid Search for hyperparameter tuning?
A) It is unable to handle discrete parameters
B) It is too quick and may miss out on evaluating some hyperparameters
C) It can be computationally expensive and time-consuming due to its exhaustive nature
D) It requires no prior knowledge of the hyperparameters
5. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A) Keras
B) TensorFlow
C) PyTorch
D) Pandas
Solutions:
| Question # 1 Answer: B,E | Question # 2 Answer: C,D | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: D |
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