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| Section | Objectives |
|---|---|
| Data Analysis and Visualization | - Data cleaning and preprocessing
|
| Data Handling and Processing with Python | - Data manipulation libraries
|
| Python Programming Fundamentals | - Basic syntax and data types
|
| Basic Statistics for Data Analysis | - Descriptive statistics
|
1. Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.
A) Developing machine learning algorithms to predict user behavior.
B) Recommending pricing strategies using predictive modeling and A/B testing.
C) Designing a data pipeline to ingest and process real-time sensor data.
D) Creating summary tables and charts to describe patterns in historical data.
2. You are given a short Python script that uses both arithmetic and assignment operators. The variable x is initialized as 4, then updated using x *= 2 + 3. What will be printed when the final value of x is displayed?
A) 20
B) 14
C) 10
D) 24
3. A loop includes a break statement when a condition is met. The programmer wants to understand how break affects loop execution. What happens when break is executed inside the loop?
A) Skips one iteration
B) Loop ends immediately
C) Loop restarts
D) Error
4. A financial technology company is reviewing its data practices to ensure legal and ethical compliance.
Which of the following actions would support responsible data handling and align with regulations like GDPR, HIPAA, and CCPA? (Choose two.)
A) Automatically opting users into data sharing unless they manually change settings.
B) Limiting data access only to marketing staff to streamline personalization.
C) Providing users with a clear explanation of how their data is used and obtaining explicit consent.
D) Retaining user data permanently unless users explicitly request deletion.
E) Implementing encryption for both stored and transmitted customer data.
5. You are reviewing a report that compares the number of weekly client meetings and project completion rates across teams. The table below summarizes the data for each team:
Which statement best describes the data shown above? Select the best answer.
A) "Gamma and Omega were outliers that inflated the meeting average, but most teams finished about 3 projects per month."
B) "Project completion numbers were nearly identical across all teams, averaging 3.25 per month."
C) "Teams with 5-6 weekly meetings tended to complete more projects than those with very high or very low meeting counts."
D) The more meetings a team had, the more projects they finished, especially in high-performing teams like Omega and Delta."
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: C,E | Question # 5 Answer: C |
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