PCAD-31-02 Certified Associate Data Analyst with Python Exam Preparation Resources

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PCAD-31-02 Certified Associate Data Analyst with Python exam is hot nowadays. Learning PCAD-31-02 exam information is a required step. There are 48 Single- and multiple-select questions, scenario-based items in real PCAD-31-02 exam, and you have 60 minutes to answer all the questions. The required passing score is 75%. The available language is English. We provide the latest Certified Associate Data Analyst with Python PCAD-31-02 exam preparation resources, which are useful for you to pass the test. Free Certified Associate Data Analyst with Python PCAD-31-02 exam preparation resources are available below.

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1. What is a major challenge in aggregating data from multiple sources?

Excessive disk space usage

Duplicate records and format inconsistencies

Increased algorithmic complexity

Overfitting in predictive models

2. What may occur if a model is evaluated using the same data it was trained on?

Overgeneralization

Data leakage

Overfitting

Underfitting

3. What is the primary goal of pre-processing data before performing analysis?

To minimize the number of data points

To obscure sensitive information

To ensure the data is clean, consistent, and suitable for modeling

To compress datasets for storage efficiency

4. Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

DataFrame.unique()

DataFrame.remove_duplicates()

DataFrame.drop_duplicates()

DataFrame.nunique()

5. Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling? (choose two)

Resetting the index after filtering

Dropping column headers

Ensuring consistent data types

Converting the DataFrame to a Series

6. Which conditions typically necessitate data normalization or scaling before analysis? (Choose two)

Features with different units and scales

Presence of duplicate data

Distance-based modeling algorithms

Boolean feature encoding

7. Which SQL commands can be used to modify the contents of a table's data directly? (Choose all that apply)

UPDATE

DELETE

SELECT

INSERT

8. Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

It overlays multiple plots in the same figure without axes

It creates a 3D surface plot using pandas

It configures multiple plots in a grid layout within a single figure

It links Matplotlib with Seaborn’s grid styling

9. Which Python constructs are useful for comparing and managing object identity in a data pipeline? (Choose all that apply)

is operator for memory-level comparison

id() function to retrieve unique object identifiers

str() function to convert data formats

== operator for checking value equivalence

10. What is a key assumption of linear regression that distinguishes it from logistic regression?

Output values must be binary

Predictors must be independent

The relationship between predictors and the outcome is linear

The target variable is unordered categorical

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11. Which SQL commands are typically used to retrieve and manipulate data in a data analysis context? (Choose all that apply)

SELECT

UPDATE

ROLLBACK

INSERT

12. Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

numpy.outer()

array1 * array2

array1.dot(array2)

numpy.matrix()

13. Which file format is most suitable for exchanging large tabular datasets with consistent column data types across systems?

.txt

.json

.csv

.xml

14. Which methods can be used to validate data types and structure in a Pandas DataFrame? (Choose two)

df.dtypes

df.info()

df.to_csv()

df.memory_usage()

15. Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

Linear regression

Logistic regression

Polynomial regression

Decision tree regression

16. Which practices enhance both readability and professionalism in spreadsheet formatting? (Choose two)

Aligning column headers to the left

Using uniform date formats across columns

Centering all data regardless of type

Applying consistent font and cell styling

17. What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

.iloc[] allows label-based selection

.loc[] does not allow slicing

.iloc[] is position-based, while .loc[] is label-based

.loc[] can only be used on Series

18. Which of the following are key differences between Pandas and NumPy arrays? (choose two)

Pandas handles labeled data; NumPy does not

NumPy supports string indexing; Pandas does not

Pandas Series are more memory efficient than NumPy arrays

Pandas integrates better with tabular data and missing values

19. Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

list

tuple

dictionary

set

20. Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

To reduce the number of iterations during training

To optimize the model’s training accuracy

To evaluate the model’s performance on unseen data

To ensure the loss function is minimized

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21. Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

One-hot encoding

Log transformation

Z-score normalization

Min-max normalization

22. How would you extract the last three rows of a DataFrame df using position-based indexing?

df.tail(3)

df.loc[-3:]

df.iloc[-3:]

df[-3]

23. Which technique is considered a primary defense against SQL injection when executing database queries from Python?

Escaping quotes manually in SQL strings

Encrypting the database connection

Using parameterized queries

Closing the database cursor after each query

24. Which of the following correctly demonstrates the use of parameterized queries using Python’s sqlite3 module?

cursor.execute("SELECT * FROM users WHERE id = " + user_id)

cursor.execute("SELECT * FROM users WHERE id = ?", user_id)

cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))

cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})

25. What is the primary purpose of using annotations in a data visualization?

To replace the need for axes

To enhance interactivity in static plots

To highlight important data points and provide contextual information

To ensure faster rendering in large datasets

26. When analyzing a dataset of customer ages, you calculate the mean, median, and mode.

What does it indicate if all three values are approximately equal?

The dataset is uniformly distributed

The dataset is positively skewed

The dataset has a normal distribution

The dataset contains outliers

27. What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?

To define global variables that persist across files

To encapsulate related data and behavior for better organization

To avoid using loops and conditional logic

To ensure all values are stored as plain strings

28. Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

Lambda functions

Global variables

Custom functions

Tuple unpacking

29. What is the outcome of the following code?

data = [5, 10, 15]

result = [x**2 for x in data if x > 5]

print(result)

[25, 100, 225]

[100, 225]

[10, 15]

[25, 100]

30. When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?

sum()

std()

skew()

mean()

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31. Which practices are typically part of the data integration process? (Choose two)

Schema alignment

Data encryption

Format standardization

Neural network training

32. Which storage system is best suited for storing and retrieving large volumes of unstructured data, such as images or logs?

Relational databases

Object storage systems

In-memory databases

Data cubes

33. What result will the following Pandas expression return: df['Age'].notnull().all()?

Returns True if all values in 'Age' are NULL

Returns True if at least one value in 'Age' is not NULL

Returns True if all values in 'Age' are not NULL

Returns the number of NULL values in 'Age'

34. What is the main purpose of validating a dataset before applying statistical analysis?

To sort the data alphabetically

To visualize patterns more clearly

To ensure the dataset meets expected quality and structure

To generate more columns from the dataset

35. Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks? (Choose two)

match-case statements

list comprehensions

decorators

while loops

36. Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

pymysql

psycopg2

sqlite3

sqlalchemy

37. Which SQL command should a data analyst use to modify existing records in a database table based on specific conditions?

INSERT

UPDATE

SELECT

CREATE

38. Why is it important to adjust data presentations based on the audience's background?

To avoid using charts altogether

To simplify all metrics to percentages only

To ensure the data is understood and supports actionable insights

To include as many technical terms as possible

39. Which packages are most commonly used together in a data science workflow for data manipulation and visualization? (Choose two)

pandas

NumPy

Pygame

flask

40. Which practices are considered part of professional Python scripting standards? (Choose two)

Including in-line comments and docstrings

Avoiding error handling to keep code clean

Using consistent indentation and naming conventions

Avoiding use of modules and libraries

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41. What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

To reduce memory usage in the dataset

To improve data visualization aesthetics

To avoid treating the same category as different due to case differences

To make string comparison operations slower

42. Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

df.dropna()

df.rename()

df.replace()

df.sort_values()

43. Which techniques are commonly used to manage type conversion between SQL and Python when importing database values? (choose two)

Using a mapping function to convert SQL types into Python objects

Ignoring data types and treating everything as strings

Defining column types in Python using sqlite3.register_converter()

Automatically casting Python variables to SQL column types without validation

44. When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

Creating samples by removing all duplicates

Generating multiple datasets of the same size by randomly sampling with replacement

Scaling all values between 0 and 1 before resampling

Drawing one sample and calculating the mean only once

45. Which file formats are commonly used for hierarchical or nested data structures in data acquisition? (Choose two)

XML

JSON

CSV

TSV

PCAD-31-02 Certified Associate Data Analyst with Python Exam Preparation Resources

PCAD-31-02 Certified Associate Data Analyst with Python Exam Preparation Resources

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