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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Evaluate the Model | 15% | - Assess classification/regression metrics - Validate model generalizability - Identify bias and overfitting |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
| Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations |
| Build the Model | 20% | - Compare and select best performing models - Perform hyperparameter tuning - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Deploy the Solution | 10% | - Ensure scalability and reliability - Deploy models as APIs in Watson - Monitor model performance post-deployment |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Prepare the Data | 18% | - Feature engineering and selection - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation |
IBM Watson Data Scientist v1 Sample Questions:
1. Cloud Pak for Data's integration with Spark allows users to:
A) Perform complex computations on small datasets only
B) Use Spark exclusively for data visualization purposes
C) Leverage distributed computing for processing large datasets efficiently
D) Avoid using any form of data processing or analysis
2. To add data assets from the catalog to a project in Cloud Pak for Data, which step is essential?
A) Assessing the compatibility of data formats
B) Selecting random data sets for variety
C) Maximizing the volume of data regardless of relevance
D) Browsing data assets based solely on their names
3. Given the Confusion matrix below, which is the formula for specificity?
A) TP/(FN + TP)
B) (TP + TN)/(FN + FP + TN + TP)
C) TP/(FP + TP)
D) TN/(TN + FP)
4. Which statistical method reduces the number of attributes by lumping highly correlated attributes together?
A) Long Short Term Memory Network (LSTM)
B) Binning
C) Synthetic Minority Over-sampling Technique (SMOTE)
D) Principal Component Analysis (PCA)
5. In the context of assessing data quality in Watson Knowledge Catalog (WKC) and Cloud Pak for Data (CPD), what is a primary focus?
A) Enhancing the graphical user interface
B) Focusing on the data's color scheme
C) Analyzing completeness, consistency, and accuracy of data
D) Increasing the volume of data collected
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: C |
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