100+ Free PCED Practice Questions

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Question 1

Score: 0/0

Which Python data structure is most appropriate for storing column-oriented tabular data with labeled rows and columns?

list

dict

numpy.ndarray

pandas.DataFrame

to track

2026 Statistics

Key Facts: PCED Exam

Exam Questions

OpenEDG

40 min

Exam Duration

OpenEDG

70%

Passing Score

OpenEDG

$59

Exam Fee

OpenEDG

Lifetime

Validity

No expiration

Online

Delivery

OpenEDG Testing Service

The PCED exam has 30 questions in 40 minutes with a 70% passing score. Domains cover Python recap (~15%), NumPy arrays (~25%), Pandas DataFrames and data wrangling (~30%), data visualization with Matplotlib and Seaborn (~15%), and descriptive statistics and data quality (~15%). Fee is $59 USD. Delivered online via OpenEDG Testing Service. Lifetime validity, no expiration. PCEP is recommended but not required.

Sample PCED Practice Questions

Try these sample questions to test your PCED exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which Python data structure is most appropriate for storing column-oriented tabular data with labeled rows and columns?

A.list

B.dict

C.numpy.ndarray

D.pandas.DataFrame

Explanation: pandas.DataFrame is a 2-D labeled tabular structure with named columns and an index for rows — exactly the shape of analytical datasets. A list is 1-D and unlabeled. A dict is unordered key/value (though it can hold columns). numpy.ndarray is unlabeled and homogeneous-dtype, so it loses column names.

2What is the canonical alias used when importing pandas in data-analysis code?

A.import pandas as pn

B.import pandas as pd

C.import pandas as ps

D.import pd from pandas

Explanation: `import pandas as pd` is the universal convention used in tutorials, books, and the official pandas documentation. Conventional aliases (np for NumPy, pd for pandas, plt for matplotlib.pyplot, sns for seaborn) make code instantly recognizable.

3Which built-in Python function reads a text file line by line in a memory-efficient way using a context manager?

A.open(path).readlines()

B.with open(path) as f: for line in f

C.file.readall()

D.io.read_lines(path)

Explanation: Iterating over an open file inside a `with` block reads one line at a time and closes the file automatically. `readlines()` loads the whole file into memory. There is no built-in `readall()` or `io.read_lines`.

4Which standard-library module is used to read and write CSV files when you do not want to load the file into a DataFrame?

A.json

B.csv

C.pickle

D.io

Explanation: The `csv` module provides `csv.reader`, `csv.writer`, `csv.DictReader`, and `csv.DictWriter` for streaming CSV without external dependencies. `json` handles JSON, `pickle` handles Python object serialization, and `io` provides the underlying stream classes.

5What does `np.arange(2, 10, 2)` return?

A.array([2, 4, 6, 8])

B.array([2, 4, 6, 8, 10])

C.array([2, 10, 2])

D.array([0, 2, 4, 6, 8])

Explanation: `np.arange(start, stop, step)` returns evenly spaced values from start (inclusive) up to stop (exclusive). With start=2, stop=10, step=2 you get [2, 4, 6, 8] — 10 is excluded.

6How many elements does `np.linspace(0, 1, 5)` produce?

A.4

B.5

C.6

D.10

Explanation: `np.linspace(start, stop, num)` returns `num` evenly spaced values, with `stop` included by default. So `np.linspace(0, 1, 5)` produces array([0. , 0.25, 0.5 , 0.75, 1. ]) — exactly 5 elements.

7What is the shape of `np.zeros((3, 4))`?

A.(4, 3)

B.(3, 4)

C.(12,)

D.(3,)

Explanation: `np.zeros(shape)` creates an array of zeros with the given shape tuple. (3, 4) means 3 rows and 4 columns, matching the tuple passed in.

8Which ndarray attribute returns the number of dimensions of an array?

A.arr.shape

B.arr.size

C.arr.ndim

D.arr.dtype

Explanation: `arr.ndim` returns the number of axes (e.g. 1 for vector, 2 for matrix). `arr.shape` returns the size along each axis as a tuple. `arr.size` is the total number of elements. `arr.dtype` is the element type.

9Given `a = np.array([[1, 2, 3], [4, 5, 6]])`, what is `a.shape`?

A.(2, 3)

B.(3, 2)

C.(6,)

D.(1, 6)

Explanation: The outer list has 2 inner lists (rows), each with 3 elements (columns), so the shape is (2, 3) — 2 rows and 3 columns.

10Which dtype does `np.array([1, 2, 3, 4])` use by default on a 64-bit system?

A.int8

B.int32

C.int64

D.float64

Explanation: NumPy infers dtype from input values. Plain Python integers are stored as the platform's default integer type, which is int64 on most 64-bit Linux/macOS systems. Pass `dtype=` to override (e.g. `dtype=np.int32`).

About the PCED Exam

The OpenEDG PCED (Certified Entry-Level Data Analyst with Python) certification validates entry-level data analysis skills using the Python data stack. It covers Python fundamentals recap, NumPy arrays, Pandas Series and DataFrame operations, data cleaning and aggregation, Matplotlib and Seaborn visualization, descriptive statistics, and Jupyter Notebook workflows. PCED is part of OpenEDG's Python specialization track that builds on PCEP fundamentals.

Questions

30 scored questions

Time Limit

40 minutes

Passing Score

70%

Exam Fee

$59 USD (OpenEDG / OpenEDG Testing Service)

PCED Exam Content Outline

~15%

Python Fundamentals (Recap)

Built-in data types, control flow, functions, comprehensions, file I/O (open, with, csv module, json module), exception handling, and standard-library essentials needed for data work

~25%

NumPy Arrays

Array creation (np.array, np.zeros, np.ones, np.arange, np.linspace), dtype, ndarray attributes (shape, ndim, size, dtype), indexing/slicing/boolean indexing, reshape/ravel/flatten/transpose, broadcasting, ufuncs, aggregations (sum/mean/min/max/std/var/argmin/argmax) with the axis parameter, np.where, sorting

~30%

Pandas DataFrames and Data Wrangling

Series, DataFrame, index, columns, dtypes; pd.read_csv / read_excel / read_json / read_sql; to_csv / to_excel; loc / iloc and boolean filtering; missing data (isna, dropna, fillna); drop_duplicates, rename, astype, replace; groupby, agg, transform, apply; merge, concat, join; pivot, pivot_table, melt; to_datetime and the dt accessor; sort_values / sort_index

~15%

Data Visualization

Matplotlib (plt.plot, plt.bar, plt.scatter, plt.hist, plt.boxplot, plt.subplot, plt.figure, plt.title/xlabel/ylabel/legend, plt.savefig) and Seaborn (sns.barplot, sns.scatterplot, sns.heatmap, sns.pairplot)

~15%

Descriptive Statistics and Data Quality

Mean, median, mode, range, variance, standard deviation, quartiles, IQR, percentiles; missing-data patterns; outlier detection (IQR rule, z-score); duplicates; basic ETL concepts; Jupyter Notebook fundamentals

How to Pass the PCED Exam

What You Need to Know

Passing score: 70%
Exam length: 30 questions
Time limit: 40 minutes
Exam fee: $59 USD

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

PCED Study Tips from Top Performers

1Master the difference between NumPy axis=0 (down rows / per column) and axis=1 (across columns / per row) — this is the single most-tested concept

2Practice Pandas loc (label-based) vs iloc (position-based) until selection feels automatic — the exam relies on it heavily

3Memorize the broadcasting rules: dimensions are compared from the right and must be equal or one of them must be 1

4Know the difference between SettingWithCopyWarning sources: chained indexing vs .loc assignment

5Practice groupby + agg + transform — the exam often shows code that uses transform to broadcast group-level stats back to original rows

6Be comfortable converting strings to datetime with pd.to_datetime and using the .dt accessor (.dt.year, .dt.month, .dt.dayofweek)

7Know IQR-based outlier detection (Q1 - 1.5*IQR, Q3 + 1.5*IQR) and the difference between population and sample variance (ddof parameter)

Frequently Asked Questions

What is the OpenEDG PCED exam?

The PCED (Certified Entry-Level Data Analyst with Python) is an entry-level data analytics certification from the OpenEDG Python Institute. It validates the ability to use Python with NumPy, Pandas, Matplotlib, and Seaborn for data wrangling, descriptive statistics, and basic visualization. PCED sits alongside PCET (testing) and PCES (security) in OpenEDG's Python specialization track.

How many questions are on the PCED exam?

The PCED exam has 30 questions to be completed in 40 minutes. Item formats include single-select, multi-select, drag-and-drop, gap-fill, and code-snippet questions. The passing score is 70%.

What is the PCED exam fee?

The PCED exam fee is $59 USD when taken through the OpenEDG Testing Service online proctoring platform. Discount vouchers are sometimes available through OpenEDG promotions and Cisco Networking Academy partnerships.

Are there prerequisites for PCED?

There are no formal prerequisites. PCEP (Certified Entry-Level Python Programmer) is strongly recommended because PCED assumes you already know Python syntax, control flow, functions, and basic data structures. The certification then focuses on the data-analysis stack on top of that base.

How should I prepare for PCED?

Plan for 40-60 hours of study over 4-6 weeks. Work through the official OpenEDG Data Analytics with Python course on edube.org. Practice NumPy array operations and broadcasting, then move to Pandas DataFrame manipulation (loc/iloc, groupby, merge, pivot). Build at least one end-to-end Jupyter notebook that loads a CSV, cleans it, aggregates it, and produces Matplotlib/Seaborn charts. Aim for 80%+ on practice questions before scheduling.

Does the PCED certification expire?

No. PCED has lifetime validity with no expiration and no continuing-education requirement, consistent with OpenEDG's other entry-level Python credentials (PCEP, PCET, PCES).