Last Updated: Aug 19, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: SQL and Data Management | ||
| Topic 2: Statistical Methods and Modeling | ||
| Topic 3: Exploratory Data Analysis | ||
| Topic 4: Data Visualization and Communication | ||
| Topic 5: Data Acquisition and Pre-Processing | 29.2% |
1. Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)
A) is operator for memory-level comparison
B) == operator for checking value equivalence
C) str() function to convert data formats
D) id() function to retrieve unique object identifiers
2. Which statements correctly describe differences between linear and logistic regression?
(Choose two)
A) Logistic regression outputs probabilities, while linear regression predicts continuous values
B) Linear regression applies only to classification tasks
C) Linear regression requires target variables to be binary
D) Logistic regression uses the logit function to model relationships
3. What is a best practice when organizing data in a spreadsheet for analysis?
A) Place charts in the middle of data tables
B) Merge cells to reduce redundancy
C) Use consistent column headers across all sheets
D) Apply conditional formatting to entire rows only
4. What is the main reason for using try-except blocks in a data analysis script that reads multiple files?
A) To enforce strict type annotations
B) To ensure functions always return None
C) To automatically restart the script after execution
D) To gracefully handle runtime errors without crashing the program
5. What does the describe() method in Pandas provide when applied to a numerical DataFrame column?
A) Basic descriptive statistics such as count, mean, and quartiles
B) Only the standard deviation of the data
C) A normalized version of the data column
D) A histogram of the data distribution
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: A,D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A |
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