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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Select appropriate ML algorithms - Perform hyperparameter tuning - Compare and select best performing models |
| Topic 2: Prepare the Data | 18% | - Feature engineering and selection - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Handle missing values and outliers |
| Topic 3: Evaluate the Model | 15% | - Validate model generalizability - Assess classification/regression metrics - Identify bias and overfitting |
| Topic 4: Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio |
| Topic 5: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 6: Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) |
| Topic 7: Deploy the Solution | 10% | - Ensure scalability and reliability - Monitor model performance post-deployment - Deploy models as APIs in Watson |
| Topic 8: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
IBM Watson Data Scientist v1 Sample Questions:
1. An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?
A) Data Understanding
B) Data Preparation
C) Modeling
D) Business Understanding
2. 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 ensures that the model is trained on the maximum amount of data possible
C) It guarantees that the model will perform with 100% accuracy on unseen data
D) It allows for the model to be validated and tested on different subsets of data to check its generalization ability
3. In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
A) It is primarily concerned with the technical deployment of solutions.
B) It focuses on maintaining and operating solutions at scale.
C) It involves the rapid building of prototypes to validate ideas.
D) It emphasizes understanding user outcomes and business needs.
4. What is a key advantage of using supervised learning techniques over unsupervised learning techniques?
A) Supervised learning can work without any labeled data.
B) Supervised learning algorithms can automatically label data.
C) Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
D) Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
5. In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
A) Choosing the most aesthetically pleasing user interface
B) Selecting the programming language with the least number of keywords
C) Determining the computational power and memory requirements for the deployed solution
D) Focusing exclusively on the cost of storage
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
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