Free Python Institute PCED-30-01 Exam Questions

Become Python Institute Certified with updated PCED-30-01 exam questions and correct answers

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Total 382 Questions | Updated On: Jun 15, 2026

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Question 1

When would you typically use the iloc attribute as opposed to the loc attribute in a pandas DataFrame?

A : When you want to apply a lambda function to each row

B : When you want to drop missing values from the DataFrame

C : When you want to access rows and columns by label

D : When you want to access rows and columns by integer-location

Answer: D

Question 2

Your dataset has multiple variables with different scales and units, making it challenging to compare them directly. You need to synthesize this data into a digestible report for decision-makers who are not data scientists. What should be your first step in preprocessing the data?

A : Perform natural language processing (NLP) on any text variables

B : Transform all variables into Z-scores

C : Conduct a chi-squared test for each variable

D : Apply hierarchical clustering to group similar variables

Answer: B

Question 3

You have a DataFrame with a 'date' column in string format ('YYYY-MM-DD'). How would you convert this column to Pandas datetime format?

A : df['date'].convert_to('datetime')

B : df['date'].astype('datetime64')

C : df['date'] = df['date'].parse('%Y-%m-%d')

D : pd.to_datetime(df['date'], format='%Y-%m-%d')

Answer: D

Question 4

After performing PCA on a dataset with 10 features, you get two principal components. Which of the following statements is true regarding these components?

A : Each principal component represents a cluster in the original dataset.

B : Principal components are scaled versions of the original features.

C : Each principal component is a linear combination of the original features.

D : The first principal component is always the most important feature in the original dataset.

Answer: C

Question 5

You are working with a large dataset containing multi-dimensional features related to customer behavior. The dataset is too large and complex to analyze easily. What technique could be most effective for reducing the dataset's dimensionality while retaining most of its original variance?

A : Principal Component Analysis (PCA)

B : One-hot encoding

C : Linear Discriminant Analysis (LDA)

D : Max-Min Scaling

Answer: A

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Total 382 Questions | Updated On: Jun 15, 2026

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