A team is developing a data analysis workflow in Jupyter Notebook. They need to ensure that the notebook can be reproduced by other team members with the same library versions. What should they do?

Save the notebook and rely on the team's default Python environment.

Export the notebook as a Python script and share that instead.

Use !pip install in the notebook to install libraries each time.

Create a requirements.txt file with pinned versions and document how to set up a virtual environment.

A machine learning engineer is training a support vector machine (SVM) with an RBF kernel on a dataset with 50,000 samples and 200 features. The training is taking too long. Which approach is most likely to reduce training time while maintaining reasonable accuracy?

Increase the number of iterations in the solver

Use a smaller value for the regularization parameter C

Use a linear kernel instead of RBF

GIAC Machine Learning Engineer

Domain 1Objective 2

Leveraging Python GMLE Practice Questions (Page 6)

Part of the Foundational Data Science and Statistics domain, which makes up ~41% of our current practice bank. GIAC (SANS) does not publish an official question count, but from its 180-minute exam (~70–120 total, ~29–49 in this domain), expect 7–12 from this objective — we provide 36 practice questions to prepare you well beyond it. (estimate)

36questions here

8free pages

8concepts

Questions 26–30

26foundation · easy
What is the shape of the array created by np.array([[1, 2, 3], [4, 5, 6]])?
Select an answer first
27application · medium
A team is developing a data analysis workflow in Jupyter Notebook. They need to ensure that the notebook can be reproduced by other team members with the same library versions. What should they do?
Select an answer first
28foundation · easy
Which Scikit-learn function is used to split a dataset into training and testing sets?
Select an answer first
29application · medium
A machine learning engineer is training a support vector machine (SVM) with an RBF kernel on a dataset with 50,000 samples and 200 features. The training is taking too long. Which approach is most likely to reduce training time while maintaining reasonable accuracy?
Select an answer first
30application · medium
A data engineer has a NumPy array of shape (100, 50) representing 100 samples with 50 features. They need to compute the mean of each feature across all samples. Which NumPy operation should they use?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to GMLE

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by GIAC (SANS). “GMLE” is a trademark of its owner, used for identification only.

Leveraging Python GMLE Practice Questions (Page 6)

Questions 26–30

What is the shape of the array created by np.array([[1, 2, 3], [4, 5, 6]])?

A team is developing a data analysis workflow in Jupyter Notebook. They need to ensure that the notebook can be reproduced by other team members with the same library versions. What should they do?

Which Scikit-learn function is used to split a dataset into training and testing sets?

A machine learning engineer is training a support vector machine (SVM) with an RBF kernel on a dataset with 50,000 samples and 200 features. The training is taking too long. Which approach is most likely to reduce training time while maintaining reasonable accuracy?

A data engineer has a NumPy array of shape (100, 50) representing 100 samples with 50 features. They need to compute the mean of each feature across all samples. Which NumPy operation should they use?

Keep studying — Foundational Data Science and Statistics