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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Monitor model performance post-deployment - Ensure scalability and reliability |
| Topic 2: Evaluate the Model | 15% | - Identify bias and overfitting - Validate model generalizability - Assess classification/regression metrics |
| Topic 3: Understand the Business Problem | 12% | - Define success metrics and constraints - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) |
| Topic 4: Prepare the Data | 18% | - Feature engineering and selection - Handle missing values and outliers - Clean, transform, and normalize datasets - Use Watson tools for data preparation |
| Topic 5: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 6: 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 7: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 8: Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Compare and select best performing models - Select appropriate ML algorithms |
IBM Watson Data Scientist v1 Sample Questions:
1. In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?
A) Decision Trees
B) K-Means Clustering
C) Support Vector Machines
D) Linear Regression
2. 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) Random Output Curve
B) Receiver Operating Characteristic
C) Recall Operation Curve
D) Regression Operation Characteristic
3. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A) PyTorch
B) Keras
C) TensorFlow
D) Pandas
4. Which statement best differentiates machine learning from deep learning?
A) Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
B) Deep learning algorithms require less data to learn.
C) Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
D) Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.
5. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?
A) conda
B) sudo
C) vim
D) pip
E) bash
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: A,D |
