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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) |
| Topic 2: 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 3: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Ensure scalability and reliability - Monitor model performance post-deployment |
| Topic 4: Evaluate the Model | 15% | - Identify bias and overfitting - Assess classification/regression metrics - Validate model generalizability |
| Topic 5: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 6: Prepare the Data | 18% | - Use Watson tools for data preparation - Handle missing values and outliers - Clean, transform, and normalize datasets - Feature engineering and selection |
| Topic 7: Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Topic 8: Build the Model | 20% | - Compare and select best performing models - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Select appropriate ML algorithms |
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) Business Understanding
C) Data Preparation
D) Modeling
2. An essential aspect of the ETL (Extract, Transform, Load) process is:
A) Transforming data exclusively in cloud environments
B) Loading data into a single, centralized database for analysis
C) Extracting the least amount of data for simplicity
D) Ensuring data quality and consistency throughout the process
3. In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?
A) Deploying all models simultaneously regardless of use case
B) Ensuring there is no logging or monitoring of model performance
C) The ability to update or retire models based on performance metrics
D) Avoiding the use of APIs for integration with applications
4. How can data splits be made reproducible in a machine learning experiment?
A) By using a consistent random seed when splitting the data
B) By partitioning the data manually
C) By splitting the data in a sequential manner without randomization
D) By using a different random seed each time the data is split
5. Which analytic technique is NOT typically used to address business requirements?
A) Regression analysis
B) Clustering
C) Decision trees
D) Proofreading
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |
