A data scientist has a dataset with 1,000 samples and a binary target with 10% positive class. They want to ensure the test set has a similar proportion of positive and negative classes as the full dataset. Which scikit-learn function parameter should they use?

train_test_split(..., stratify=y)

train_test_split(..., test_size=0.2)

train_test_split(..., shuffle=False)

train_test_split(..., random_state=42)

A data scientist trains a decision tree on a training set and achieves 99% accuracy, but only 70% accuracy on the test set. What is the most likely explanation?

The model is overfitting the training data.

The model is underfitting the training data.

A data scientist is training a model on a dataset with 5,000 samples. To ensure the model generalizes well, they split the data into training and test sets. Which practice is most important to prevent overfitting?

Keep the test set separate and use it only for final evaluation.

Use a large test set and a small training set.

Train the model on the test set to improve accuracy.

Use the same data for training and testing to get more samples.

Python InstitutePCAD - Certified Associate in Data Analytics with Python

Domain 4Objective 2

Statistical Methods and Machine Learning PCAD-31 Practice Questions (Page 5)

Part of the Data Analysis and Modeling domain, which accounts for 18.8% of the PCAD-31 exam. Python Institute does not publish an official question count, but from its 60-minute exam (~25–40 total, ~5–5 in this domain), expect 3–3 from this objective — we provide 30 practice questions to prepare you well beyond it. (estimate)

30questions here

6free pages

9concepts

18.8%of the exam

Questions 21–25

21application · easy
A data analyst wants to compute the skewness of a dataset using scipy. Which function should they use?
Select an answer first
22application · medium
A data scientist has a dataset with 1,000 samples and a binary target with 10% positive class. They want to ensure the test set has a similar proportion of positive and negative classes as the full dataset. Which scikit-learn function parameter should they use?
Select an answer first
23application · easy
A data scientist trains a decision tree on a training set and achieves 99% accuracy, but only 70% accuracy on the test set. What is the most likely explanation?
Select an answer first
24application · medium
A data analyst is evaluating a classification model. They have computed accuracy, precision, recall, and F1-score. The dataset is highly imbalanced (95% negative class). Which metric is most informative for assessing model performance?
Select an answer first
25application · easy
A data scientist is training a model on a dataset with 5,000 samples. To ensure the model generalizes well, they split the data into training and test sets. Which practice is most important to prevent overfitting?
Select an answer first

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Statistical Methods and Machine Learning PCAD-31 Practice Questions (Page 5)

Questions 21–25

A data analyst wants to compute the skewness of a dataset using scipy. Which function should they use?

A data scientist has a dataset with 1,000 samples and a binary target with 10% positive class. They want to ensure the test set has a similar proportion of positive and negative classes as the full dataset. Which scikit-learn function parameter should they use?

A data scientist trains a decision tree on a training set and achieves 99% accuracy, but only 70% accuracy on the test set. What is the most likely explanation?

A data analyst is evaluating a classification model. They have computed accuracy, precision, recall, and F1-score. The dataset is highly imbalanced (95% negative class). Which metric is most informative for assessing model performance?

A data scientist is training a model on a dataset with 5,000 samples. To ensure the model generalizes well, they split the data into training and test sets. Which practice is most important to prevent overfitting?

Keep studying — Data Analysis and Modeling