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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Handling and Processing with Python	- Data manipulation libraries 1. NumPy basics 2. Pandas fundamentals - Working with data structures 1. Iterating and transformations 2. Lists, tuples, dictionaries
Topic 2: Basic Statistics for Data Analysis	- Descriptive statistics 1. Mean, median, mode 2. Variance and standard deviation
Topic 3: Python Programming Fundamentals	- Basic syntax and data types 1. Control flow basics 2. Variables and operators
Topic 4: Data Analysis and Visualization	- Data cleaning and preprocessing 1. Missing values handling 2. Data transformation techniques - Data visualization 1. Matplotlib basics 2. Plot interpretation

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are given the following list of daily step counts:
steps = [8230, 9020, 7640, 8760, 10020, 2546, 9817]
Your task is to calculate:
- the standard deviation of the step counts,
- the average rounded up to the nearest whole number, and
- the median of the step counts.
Which code snippet correctly performs all three tasks? Select the best answer.
import statistics

A. import math
print(statistics.stdev(steps))
print(math.ceil(statistics.mean(steps)))
print(statistics.median(steps))
B. import math
print(math.stdev(steps))
print(statistics.mean(steps))
print(statistics.median(steps))
import statistics
C. import math
print(statistics.stdev(steps))
print(round(math.mean(steps)))
print(math.median(steps))
import statistics
D. import math
print(statistics.variance(steps))
print(math.ceil(sum(steps) / len(steps)))
print(math.floor(statistics.median(steps)))
import statistics

Answer: A

Explanation:
It uses statistics.stdev() to compute the standard deviation, statistics.mean() to compute the average and math.ceil() to round it up to the nearest whole number, and statistics.median() to compute the median.

NEW QUESTION # 18
A dictionary is defined as d = {"a":1, "b":2}. The programmer accesses d["c"]. What happens when this line executes?

A. Adds key automatically
B. Returns None
C. Raises KeyError
D. Returns 0

Answer: C

Explanation:
Accessing a non-existent key using square brackets raises a KeyError. Python does not automatically create missing keys, so the program will terminate unless the exception is handled.

NEW QUESTION # 19
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
B. 1, 2, 3, 4, 5
C. 2, 4, 6
D. 1, 3, 5

Answer: D

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 # 20
You are analyzing survey results from students about their favorite colors. The list colorsstores individual responses:

You want to:
- find the number of unique colors mentioned using NumPy, and
- determine how often each color was chosen using Counter.
Which code snippet correctly performs both tasks? Select the best answer.
from numpy import unique

A. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
B. from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np
C. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
D. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)

Answer: A

Explanation:
numpy.unique returns the distinct values in the list, and taking its length gives the number of unique colors. Counter(colors) correctly counts how many times each color appears in the responses.

NEW QUESTION # 21
An e-commerce site records every product view and purchase.
Which of the following best describes how these logs become marketing insights?

A. Raw event logs are stored, archived, and indexed - like by user ID and session - which then facilitate audit trails.
B. Raw event logs are validated, organized, and compiled into datasets - like timestamped user actions - which then support system debugging.
C. Raw event logs are filtered, aggregated, and formatted into summaries - like total visits per category - which then provide traffic reports.
D. Raw event logs are structured, analyzed, and interpreted into information - like conversion rates by product - which then builds knowledge of customer preferences.

Answer: D

Explanation:
Marketing insights are produced when raw event logs are structured, analyzed, and interpreted to generate meaningful metrics such as conversion rates. These interpreted results transform data into actionable knowledge about customer behavior and preferences.

NEW QUESTION # 22
......

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