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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints |
| Evaluate the Model | 15% | - Validate model generalizability - Identify bias and overfitting - Assess classification/regression metrics |
| Deploy the Solution | 10% | - Deploy models as APIs in Watson - Ensure scalability and reliability - Monitor model performance post-deployment |
| Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Select appropriate ML algorithms - Compare and select best performing models |
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Prepare the Data | 18% | - Use Watson tools for data preparation - Feature engineering and selection - Handle missing values and outliers - Clean, transform, and normalize datasets |
| 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 |
IBM Watson Data Scientist v1 Sample Questions:
1. Which analytic technique is NOT typically used to address business requirements?
A) Proofreading
B) Clustering
C) Decision trees
D) Regression analysis
2. Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
A) Sequential Search
B) Binary Search
C) Grid Search
D) Random Search
3. Which of the following is true regarding cross-validation?
A) It helps in identifying the model's performance variability across different data splits.
B) It involves training the model on the entire dataset at once.
C) It should be avoided as it leads to overfitting.
D) It decreases the variability of the model performance estimation.
4. The ROC curve is a graphical representation that shows the performance of a classification model at all classification thresholds.
What does ROC stand for?
A) Recall Operation Curve
B) Receiver Operating Characteristic
C) Random Output Curve
D) Regression Operation Characteristic
5. Which statement best differentiates machine learning from deep learning?
A) Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.
B) Deep learning algorithms require less data to learn.
C) Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
D) Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: A,D | Question # 4 Answer: B | Question # 5 Answer: D |
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