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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 2: Understand the Business Problem | 12% | - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) - Define success metrics and constraints |
| Topic 3: Build the Model | 20% | - Compare and select best performing models - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Select appropriate ML algorithms |
| Topic 4: Deploy the Solution | 10% | - Monitor model performance post-deployment - Deploy models as APIs in Watson - Ensure scalability and reliability |
| Topic 5: Collect and Explore the Data | 15% | - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations - Identify and access data sources in Watson Studio |
| Topic 6: Evaluate the Model | 15% | - Validate model generalizability - Assess classification/regression metrics - Identify bias and overfitting |
| Topic 7: Prepare the Data | 18% | - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Handle missing values and outliers - Feature engineering and selection |
| Topic 8: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
IBM Watson Data Scientist v1 Sample Questions:
Which statement describes bagging?
- A. Building models in parallel and aggregating their predictions to select the final prediction.
- B. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
- C. Building models and using their output as features into a final model.
- D. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
Correct Answer: A 🗳️
In classification models, which of the following metrics is NOT directly derived from the confusion matrix?
- A. F1-score
- B. Recall
- C. Mean Absolute Error (MAE)
- D. Precision
Correct Answer: C 🗳️
What is a benefit of creating data pipelines to automate the model lifecycle?
- A. Encourages a one-size-fits-all approach to model development
- B. It necessitates frequent manual updates and checks
- C. It provides a structured approach to processing, validating, and deploying models
- D. Reduces the need for understanding the underlying data
Correct Answer: C 🗳️
Cloud Pak for Data's integration with Spark allows users to:
- A. Use Spark exclusively for data visualization purposes
- B. Leverage distributed computing for processing large datasets efficiently
- C. Perform complex computations on small datasets only
- D. Avoid using any form of data processing or analysis
Correct Answer: B 🗳️
What is the primary purpose of partitioning data into training and test sets?
- A. To evaluate the model's performance on unseen data
- B. To ensure that the model gets exposed to all possible data scenarios during training
- C. To maximize the accuracy of the model by using all data for training
- D. To increase the computational efficiency of model training
Correct Answer: A 🗳️
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Gloria -
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Thank you for the dump IBM Watson Data Scientist v1