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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Supervised model creation (decision trees, ensembles, SVM, neural networks) - Model comparison and selection |
| Topic 2: Data Sources | 30-36% | - Exploring and modifying data - Importing and preparing data - Dimensionality reduction and feature engineering |
| Topic 3: Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
1. When deploying a machine learning model, what is meant by "model latency"?
A) The time it takes for the model to make predictions once deployed
B) The time it takes to build a model
C) The time it takes to train a model
D) The time it takes to create synthetic data
2. When deploying a model, what is "model explainability"?
A) The time it takes to make predictions
B) The capability to interpret and understand the model's decisions and predictions
C) The simplicity of the model
D) The process of data preprocessing
3. In the context of data sources, what is ETL?
A) Efficient Text Link
B) Examine, Test, Log
C) Extract, Transform, Load
D) Execute, Terminate, Launch
4. In natural language processing (NLP), what is a common preprocessing step for text data before building models?
A) Standardization
B) One-Hot Encoding
C) Tokenization
D) Principal Component Analysis (PCA)
5. What is the purpose of cross-validation in model building and evaluation?
A) Generating synthetic data
B) Reducing the dataset size
C) Assessing the model's generalization performance
D) Splitting the dataset into training and testing sets
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: C |
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