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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
| Topic 2: Evaluate the Model | 15% | - Validate model generalizability - Identify bias and overfitting - Assess classification/regression metrics |
| 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: 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 5: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 6: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 7: Build the Model | 20% | - Compare and select best performing models - Train models using Watson AutoAI and SPSS - Select appropriate ML algorithms - Perform hyperparameter tuning |
| Topic 8: Prepare the Data | 18% | - Use Watson tools for data preparation - Clean, transform, and normalize datasets - Handle missing values and outliers - Feature engineering and selection |
IBM Watson Data Scientist v1 Sample Questions:
Question #1
The process of aligning on user intents for a solution involves:
A. Determining the technical feasibility exclusively
B. Identifying and understanding the needs and goals of end-users
C. Focusing on the data management strategies
D. Understanding the business model in depth
Question #2
Automating data processing and model deployment with jobs in Watson Studio helps to:
A. Increase the need for manual intervention in the model lifecycle
B. Limit the ability to update models based on new data
C. Enhance the reproducibility and efficiency of model deployments
D. Reduce the scalability of deployed solutions
Question #3
In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?
A. It increases the computational complexity without improving model performance
B. It guarantees that the model will perform with 100% accuracy on unseen data
C. It allows for the model to be validated and tested on different subsets of data to check its generalization ability
D. It ensures that the model is trained on the maximum amount of data possible
Question #4
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
Question #5
In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?
A. Ensuring there is no logging or monitoring of model performance
B. The ability to update or retire models based on performance metrics
C. Avoiding the use of APIs for integration with applications
D. Deploying all models simultaneously regardless of use case
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: C | Question #3 Correct Answer: C | Question #4 Correct Answer: C | Question #5 Correct Answer: B |
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