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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build the Model | 20% | - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Compare and select best performing models |
| Topic 2: Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints |
| Topic 3: Deploy the Solution | 10% | - Monitor model performance post-deployment - Deploy models as APIs in Watson - Ensure scalability and reliability |
| Topic 4: Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| Topic 5: Prepare the Data | 18% | - Use Watson tools for data preparation - Feature engineering and selection - Handle missing values and outliers - Clean, transform, and normalize datasets |
| Topic 6: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 7: 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 8: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
IBM Watson Data Scientist v1 Sample Questions:
1. Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
A) Binary Search
B) Random Search
C) Grid Search
D) Sequential Search
2. In Cognos Analytics, which two features distinguish stories from dashboards?
A) Stories convey a conclusion.
B) Stories are text-based and do not contain visualizations.
C) Stories automatically load different filters for different users.
D) Stories provide a narrative over time.
E) Stories can be embedded in websites or documents.
3. Which hyperparameter is NOT commonly adjusted in a deep learning model?
A) Learning rate
B) The color of the model's output
C) Activation function
D) Number of layers
4. Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?
A) Filter
B) Order
C) Condition
D) Key
5. In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?
A) Deploying all models simultaneously regardless of use case
B) The ability to update or retire models based on performance metrics
C) Avoiding the use of APIs for integration with applications
D) Ensuring there is no logging or monitoring of model performance
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,D | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: B |
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