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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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                          Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q20-Q25):

                          NEW QUESTION # 20
                          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?

                          Answer: B

                          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 # 21
                          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.

                          Answer: C

                          Explanation:
                          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.


                          NEW QUESTION # 22
                          Below are six possible summaries for your presentation:
                          - Summary 1: "The data revealed a moderate positive correlation between age and recycling frequency. While 72% of students reported recycling paper regularly, only 39% reported recycling plastic."
                          - Summary 2: "Analysis showed notable differences in recycling habits by material type, with paper being most recycled and plastic least recycled."
                          - Summary 3: "Older kids recycled more than younger ones. Paper was the most recycled item- plastic got forgotten a lot."
                          - Summary 4: "Student responses showed positive environmental habits overall, though participation varied by waste type."
                          - Summary 5: "A lot of students said they recycle. Some didn't answer every question, so we couldn't figure out everything."
                          - Summary 6: "We asked more than 300 students about recycling. Most said they recycle paper, but way fewer recycle plastic." Which communication style is the best fit for Group A (data scientists) and Group B (12-year- olds), respectively? Select the best answer.

                          Answer: B

                          Explanation:
                          The first summary uses precise statistical language and quantitative detail appropriate for data scientists. The third summary uses simpler, more conversational language that is easier for 12- year-olds to understand.


                          NEW QUESTION # 23
                          You are working with city names entered by users. These names may contain inconsistent capitalization and unwanted spaces.
                          To standardize the data, you want to:
                          - Remove any leading or trailing whitespace, and
                          - Capitalize the first letter of each word (e.g., convert "new york" to "New York").
                          For example:
                          " New york " → "New York"
                          "lOS ANGELES" → "Los Angeles"
                          You are given a variable citythat contains the raw input.
                          Which line of code correctly updates the value of cleaned_cityto apply the required transformation? Select the best answer.

                          Answer: A

                          Explanation:
                          Stripping removes leading and trailing whitespace, and applying title formatting capitalizes the first letter of each word while converting the remaining letters to lowercase, producing the correctly standardized city name.


                          NEW QUESTION # 24
                          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

                          Answer: D

                          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 # 25
                          ......

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