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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Introduction to Data and Data Analysis Concepts	22.5%	- Data Ethics and Privacy 1. Understand basic data privacy concepts 2. Recognize ethical considerations in data handling - Define and Classify Data 1. Differentiate structured, semi-structured, and unstructured data 2. Explain how data becomes meaningful 3. Classify data as quantitative or qualitative - Data Analysis Process and Workflow 1. Describe the steps of the data analysis process 2. Identify common data sources and collection methods 3. Explain the role of data cleaning and preparation
Python Basics for Data Analysis	32.5%	- Data Structures 1. Work with lists, tuples, dictionaries, and sets 2. Apply common operations and methods to data structures - File Handling 1. Use the csv module for basic CSV operations 2. Read from and write to files (text, CSV) - Python Fundamentals 1. Use variables, data types, and basic operators 2. Implement control flow structures (loops, conditionals) 3. Define and use functions
Working with Data and Performing Simple Analyses	32.5%	- Data Analysis with Python Libraries 1. Use the math and statistics modules for basic calculations 2. Perform basic operations with NumPy arrays 3. Work with the datetime module for date/time data 4. Utilize the collections module for specialized containers - Data Cleaning and Transformation 1. Perform basic data transformation (filtering, sorting, grouping) 2. Handle missing and inconsistent data - Simple Analytical Techniques 1. Calculate descriptive statistics (mean, median, mode) 2. Identify basic patterns and trends in data
Communicating Insights and Reporting	12.5%	- Data Storytelling and Reporting 1. Create clear and concise analytical reports 2. Structure insights as a narrative 3. Present insights with visual and verbal techniques - Data Visualization 1. Select appropriate visuals for different data types 2. Recognize common visualization types (bar, line, pie charts) 3. Interpret simple data visualizations

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Python Institute PCED - Certified Entry-Level Data Analyst with Python PCED-30-02 Prüfungsfragen mit Lösungen (Q26-Q31):

26. Frage
How do the analysis and visualization stages of the data lifecycle typically work together?

A. Analysis summarizes archived data, and visualization transforms it into structured tables for storage.
B. Analysis produces charts and graphs, while visualization interprets the data to detect outliers.
C. Analysis identifies patterns and insights in processed data, which are then communicated through visualizations for informed decision-making.
D. Analysis retrieves raw inputs from dashboards, while visualization uses that input to clean the data.

Antwort: C

Begründung:
Analysis involves examining processed data to uncover patterns, trends, and insights, and visualization presents those findings in graphical form so stakeholders can clearly understand and act on the results.

27. Frage
Which of the following statements about Python functions are correct? (Choose two.)

A. If a function does not contain a returnstatement, it returns Noneby default.
B. Default parameters must always come before required ones in the parameter list.
C. passexecutes the function body and returns its result.
D. Positional arguments must be passed in the order defined, while keyword arguments can be passed in any order using their parameter names.
E. Functions can only return numeric values or strings.
F. A function cannot call itself or be passed as an argument to itself.

Antwort: A,D

Begründung:
Positional arguments are matched to parameters by their position, so they must follow the defined order, while keyword arguments are matched by name and can be provided in any order. If a function completes without an explicit return statement, Python returns None automatically.

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

Antwort: B

Begründung:
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.

29. Frage
A list is defined as [1, 2, 3]. The programmer uses the method .append([4, 5]). What will the resulting list look like after execution?

A. [1,2,3,[4,5]]
B. Error
C. [1,2,3,4,5]
D. [4,5,1,2,3]

Antwort: A

Begründung:
The append() method adds its argument as a single element. Therefore, the list [4, 5] is added as one nested list, resulting in [1, 2, 3, [4, 5]].

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

Antwort: B

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

31. Frage
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