PCED-30-02 Fragen&Antworten & PCED-30-02 Fragen Antworten

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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Working with Data and Performing Simple Analysis25%- Data cleaning and preparation
  • 1. Filtering, sorting, transforming data
  • 2. Formatting and standardizing values
  • 3. Handling missing values, duplicates, and errors
- Exploratory data analysis
  • 1. Calculating mean, median, mode, range, variance, standard deviation
  • 2. Identifying patterns, trends, and outliers
- Data aggregation and grouping
  • 1. Summarizing and grouping datasets
- Data acquisition and loading
  • 1. Importing data from external sources
  • 2. Reading text, CSV, and structured files
Data Visualization and Communication20%- Interpreting and presenting results
  • 1. Reporting findings clearly and concisely
  • 2. Deriving conclusions and insights
- Principles of effective data visualization
  • 1. Clarity, simplicity, and accuracy
  • 2. Choosing appropriate chart types
- Creating basic visualizations
  • 1. Using text and simple plotting tools
  • 2. Line charts, bar charts, histograms, pie charts
Python Basics for Data Analysis32.5%- Introduction to NumPy
  • 1. Arrays, basic operations, indexing, slicing
- Built-in modules for data work
  • 1. math, statistics, datetime, collections, csv
- Core Python syntax and data types
  • 1. Variables, numbers, strings, booleans
  • 2. Lists, tuples, sets, dictionaries
- Control flow and functions
  • 1. Conditional statements, loops, iteration
  • 2. Basic exception handling
  • 3. Defining and calling functions, parameters, return values
Introduction to Data and Data Analysis Concepts22.5%- Definition and classification of data
  • 1. Process of turning raw data into insights
  • 2. Role of data in decision-making and business
  • 3. Difference between data, information, and knowledge
- Basic statistical concepts
  • 1. Descriptive vs inferential statistics
  • 2. Population, sample, variable, observation
- Data types and measurement scales
  • 1. Nominal, ordinal, interval, ratio scales
  • 2. Qualitative vs quantitative data
- Data lifecycle and ethical considerations
  • 1. Privacy, security, bias, and fairness in data
  • 2. Data collection, storage, processing, usage, and sharing

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Python Institute PCED - Certified Entry-Level Data Analyst with Python PCED-30-02 Prüfungsfragen mit Lösungen (Q33-Q38):

33. Frage
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

Antwort: C

Begründung:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.


34. Frage
A list is reversed using slicing: lst[::-1]. The programmer wants to confirm whether this modifies the original list or creates a new one. What is the correct behavior?

Antwort: A

Begründung:
Slicing creates a new list object rather than modifying the original. The expression lst[::-1] returns a reversed copy, leaving the original list unchanged.


35. Frage
A government agency collects citizen data for various public services.
What is the primary reason for emphasizing careful data management throughout the entire data lifecycle in this context? Select the best answer.

Antwort: B

Begründung:
Careful data management across the entire lifecycle ensures high data quality for reliable public services, protects sensitive citizen information from misuse, and maintains compliance with legal and regulatory data protection requirements.


36. Frage
You are creating a presentation slide to communicate findings about student stress levels across four categories over a six-month period. You include the following line graph in your presentation:

Your goal is to help your audience clearly understand trends in stress levels over time.
What is the highest problem with this visual presentation? Select the best answer.

Antwort: C

Begründung:
The lines are very light and visually similar, making it difficult to clearly distinguish between the different stress level trends, which directly reduces the effectiveness of the visualization in communicating patterns over time.


37. Frage
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

Antwort: D

Begründung:
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.


38. Frage
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