A data science team is collaborating on a Jupyter Notebook that contains data cleaning and analysis steps. They want to ensure that the notebook runs correctly from top to bottom and that outputs are reproducible. Which practice is most important?

Use global variables to store intermediate results.

Clear all outputs before sharing to reduce file size.

Run the notebook in a fresh kernel and execute all cells in order before sharing.

Use magic commands like %matplotlib inline to ensure plots display.

A data analyst is working with a DataFrame that has a MultiIndex of (year, month). They need to compute the year-over-year percentage change in total sales for each month. Which operation correctly computes this?

df.groupby(level='month')['sales'].pct_change()

df.groupby(level='year')['sales'].pct_change()

df.unstack().pct_change(axis=1)

A data analyst has a DataFrame with columns 'category' and 'value'. They want to create a bar plot showing the mean value for each category, with error bars representing the standard error of the mean. Which Seaborn function is most appropriate?

sns.boxplot(data=df, x='category', y='value')

sns.violinplot(data=df, x='category', y='value')

sns.countplot(data=df, x='category')

sns.barplot(data=df, x='category', y='value')

A financial analyst has a DataFrame with columns 'date', 'region', and 'sales'. They need to create a line plot showing monthly total sales per region, with each region as a separate line. Which Pandas/Matplotlib approach achieves this?

df.groupby('region')['sales'].sum().plot()

df.set_index('date').groupby('region')['sales'].plot()

df.pivot_table(index='date', columns='region', values='sales').plot()

df.groupby(['region', pd.Grouper(key='date', freq='M')])['sales'].sum().unstack().plot()

A data analyst is creating a visualization to compare the distribution of a numeric variable across three different groups. The dataset has 10,000 rows per group. They want to show the median, quartiles, and potential outliers. Which plot is most appropriate?

A line plot (sns.lineplot) with the mean for each group

A bar plot (sns.barplot) with the mean and error bars

A box plot (sns.boxplot) for each group

A scatter plot (sns.scatterplot) with jitter

GIAC Machine Learning Engineer

Domain 1Objective 2

Leveraging Python GMLE Practice Questions (Page 1)

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

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Questions 1–5

1expert · hard
A data science team is collaborating on a Jupyter Notebook that contains data cleaning and analysis steps. They want to ensure that the notebook runs correctly from top to bottom and that outputs are reproducible. Which practice is most important?
Select an answer first
2expert · hard
A data analyst is working with a DataFrame that has a MultiIndex of (year, month). They need to compute the year-over-year percentage change in total sales for each month. Which operation correctly computes this?
Select an answer first
3application · medium
A data analyst has a DataFrame with columns 'category' and 'value'. They want to create a bar plot showing the mean value for each category, with error bars representing the standard error of the mean. Which Seaborn function is most appropriate?
Select an answer first
4application · medium
A financial analyst has a DataFrame with columns 'date', 'region', and 'sales'. They need to create a line plot showing monthly total sales per region, with each region as a separate line. Which Pandas/Matplotlib approach achieves this?
Select an answer first
5expert · hard
A data analyst is creating a visualization to compare the distribution of a numeric variable across three different groups. The dataset has 10,000 rows per group. They want to show the median, quartiles, and potential outliers. Which plot is most appropriate?
Select an answer first

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