Exam Name: IBM Watson Data Scientist v1
Certification Provider: IBM
Corresponding Certification: IBM Certification
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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Preparation and Analysis | - Data cleaning and preprocessing - Feature engineering basics - Exploratory data analysis |
| Topic 2: Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
| Topic 3: Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Topic 4: Machine Learning Methods | - Unsupervised learning - Model evaluation and validation - Supervised learning |
| Topic 5: IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
IBM Watson Data Scientist v1 Sample Questions:
An essential aspect of the ETL (Extract, Transform, Load) process is:
- A. Transforming data exclusively in cloud environments
- B. Ensuring data quality and consistency throughout the process
- C. Extracting the least amount of data for simplicity
- D. Loading data into a single, centralized database for analysis
Correct Answer: B 🗳️
Key metrics for a solution should be defined based on:
- A. The most recent technological trends
- B. The personal preferences of the project stakeholders
- C. The number of available data scientists
- D. The specific objectives and desired outcomes of the project
Correct Answer: D 🗳️
Which statement describes bagging?
- A. Building models and using their output as features into a final model.
- B. Building models in parallel and aggregating their predictions to select the final prediction.
- C. Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
- D. Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
Correct Answer: B 🗳️
What is the primary purpose of partitioning data into training and test sets?
- A. To ensure that the model gets exposed to all possible data scenarios during training
- B. To evaluate the model's performance on unseen data
- 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: B 🗳️
Given the Confusion matrix below, which is the formula for specificity?
- A. TP/(FN + TP)
- B. (TP + TN)/(FN + FP + TN + TP)
- C. TN/(TN + FP)
- D. TP/(FP + TP)
Correct Answer: C 🗳️
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