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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Objectives
Data Handling and Processing with Python	- Data manipulation libraries 1. Pandas fundamentals 2. NumPy basics - Working with data structures 1. Iterating and transformations 2. Lists, tuples, dictionaries
Basic Statistics for Data Analysis	- Descriptive statistics 1. Variance and standard deviation 2. Mean, median, mode
Data Analysis and Visualization	- Data cleaning and preprocessing 1. Data transformation techniques 2. Missing values handling - Data visualization 1. Matplotlib basics 2. Plot interpretation
Python Programming Fundamentals	- Basic syntax and data types 1. Control flow basics 2. Variables and operators

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q42-Q47):

NEW QUESTION # 42
A healthcare provider analyzes patient records to determine that appointment cancellations spike during weather alerts and flu season.
What type of analytics is this?

A. Predictive analytics - it uses weather and illness trends to forecast future cancellations.
B. Prescriptive analytics - it automates patient rescheduling based on cancellation patterns.
C. Descriptive analytics - it reports appointment numbers across past seasons.
D. Diagnostic analytics - it identifies reasons behind patterns in patient behavior.

Answer: D

Explanation:
It examines existing data to understand why cancellations increase, identifying underlying causes such as weather alerts and flu season, which characterizes diagnostic analytics.

NEW QUESTION # 43
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. Creating summary tables and charts to describe patterns in historical data.
B. Developing machine learning algorithms to predict user behavior.
C. Designing a data pipeline to ingest and process real-time sensor data.
D. Recommending pricing strategies using predictive modeling and A/B testing.

Answer: A

Explanation:
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.

NEW QUESTION # 44
A loop is designed using range(1, 6, 2) to iterate through numbers. The developer wants to know exactly which values will be generated during execution. Which sequence correctly represents the values produced?

A. 1, 3, 5
B. 1, 3
C. 1, 2, 3, 4, 5
D. 2, 4, 6

Answer: A

Explanation:
The range(start, stop, step) function generates numbers starting from 1, increasing by 2, and stopping before 6. Therefore, the resulting sequence is 1, 3, and 5.

NEW QUESTION # 45
A data analyst exports a cleaned dataset from Python and wants to share it with a colleague for easy use in Excel.
Which file format is best suited for this purpose? Select the best answer.

A. SQL, because it directly converts tabular data into database tables that Excel can open.
B. CSV, because it stores tabular data in plain text and is compatible with spreadsheet applications.
C. JSON, because it stores hierarchical data and is designed for visual presentation in Excel.
D. XML, because it automatically preserves spreadsheet formatting and charts.

Answer: B

Explanation:
CSV stores tabular data in a simple plain-text, comma-separated format that is widely supported by spreadsheet applications like Excel, making it ideal for sharing datasets for easy import and analysis.

NEW QUESTION # 46
You are developing a temperature control module for a laboratory incubator. Your objectives are to:
- generate timestamps every 10 minutes over a 3-hour span (i.e., 0 to 180 minutes), and
- simulate five evenly spaced target temperatures between 35.0°C and 37.0°C for system calibration.
Which code snippet correctly produces both sequences using NumPy? Select the best answer.
import numpy as np

A. timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
B. timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np
C. timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np
D. timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np

Answer: A

Explanation:
np.arange(0, 181, 10) generates timestamps from 0 through 180 in 10-minute steps (end is excluded, so using 181 includes 180). np.linspace(35.0, 37.0, 5) generates five evenly spaced temperatures including both 35.0°C and 37.0°C.

NEW QUESTION # 47
......

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