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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: Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
| Topic 3: Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Importing and preparing data - Exploring and modifying data |
1. Which statements are true for the F1 score?
(Choose 2.)
A) F1 score is calculated based on a cut off value.
B) F1 score is calculated based on a depth value.
C) F1 score is applicable to a model with an interval target.
D) F1 score is applicable to a model with a binary target.
2. What is the purpose of data profiling in data source management?
A) To assess the quality and characteristics of data
B) To optimize data storage
C) To execute data queries
D) To create data visualizations
3. What is a data lake architecture designed to store primarily?
A) Data from a single source or department
B) Highly structured data in tabular format
C) All types of data, including structured and unstructured data
D) Only unstructured data in raw form
4. What is the primary role of a data warehouse in an organization?
A) Data transformation and cleaning
B) Data exploration and visualization
C) Long-term data storage and analysis
D) Real-time data analysis
5. In the context of data sources, what is meant by data versioning?
A) Compressing data to reduce storage space
B) Encrypting data to protect against unauthorized access
C) Storing multiple copies of the same data to increase redundancy
D) Keeping track of different versions or changes to data over time
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: D |
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