A data analyst has a DataFrame with a 'date' column that contains datetime objects and a 'value' column. The analyst wants to resample the data to a weekly frequency, computing the sum of 'value' for each week. The DataFrame is not sorted by date. Which sequence should be used?

Use groupby(pd.Grouper(key='date', freq='W')).sum() without sorting.

Set 'date' as index, sort_index(), then resample('W').sum()

Resample directly on the 'date' column without setting it as index.

Sort the DataFrame by 'date', then resample('W').sum() without setting the index.

A data analyst has a DataFrame with a 'category' column and a 'value' column. The analyst wants to create a bar plot showing the mean value for each category, with error bars representing the standard deviation. Which approach should be used?

Use seaborn's pointplot with x='category', y='value', and ci='sd'.

Use pandas' plot.bar with yerr=df.groupby('category')['value'].std() and compute the mean using groupby.

Use matplotlib's plt.bar with the raw 'value' column and no aggregation.

Use seaborn's barplot with x='category', y='value', and default error bars.

GIAC Python Coder

Domain 4Objective 1

Data Analysis with Python GPYC Practice Questions (Page 2)

Part of the Data Analysis domain, which makes up ~9% of our current practice bank. GIAC (SANS) does not publish an official question count, but from its 120-minute exam (~50–80 total, ~5–7 in this domain), expect 5–7 from this objective — we provide 33 practice questions to prepare you well beyond it. (estimate)

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Questions 6–10

6expert · hard
A data analyst has a DataFrame with a 'date' column that contains datetime objects and a 'value' column. The analyst wants to resample the data to a weekly frequency, computing the sum of 'value' for each week. The DataFrame is not sorted by date. Which sequence should be used?
Select an answer first
7expert · hard
A data analyst has a DataFrame with a 'category' column and a 'value' column. The analyst wants to create a bar plot showing the mean value for each category, with error bars representing the standard deviation. Which approach should be used?
Select an answer first
8expert · hard
A security analyst wants to visualize the relationship between two continuous variables, 'packet_size' and 'latency', but the dataset contains many overlapping points. Which plotting technique is most effective for revealing the density of points?
Select an answer first
9foundation · easy
Which pandas method removes duplicate rows from a DataFrame?
Select an answer first
10expert · hard
A data analyst has a DataFrame with two columns: 'group' (A or B) and 'score'. The analyst wants to test if the mean scores of the two groups are significantly different. The data is normally distributed and the variances are equal. Which statistical test should be used?
Select an answer first

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Data Analysis with Python GPYC Practice Questions (Page 2)

Questions 6–10

A data analyst has a DataFrame with a 'date' column that contains datetime objects and a 'value' column. The analyst wants to resample the data to a weekly frequency, computing the sum of 'value' for each week. The DataFrame is not sorted by date. Which sequence should be used?

A data analyst has a DataFrame with a 'category' column and a 'value' column. The analyst wants to create a bar plot showing the mean value for each category, with error bars representing the standard deviation. Which approach should be used?

A security analyst wants to visualize the relationship between two continuous variables, 'packet_size' and 'latency', but the dataset contains many overlapping points. Which plotting technique is most effective for revealing the density of points?

Which pandas method removes duplicate rows from a DataFrame?

A data analyst has a DataFrame with two columns: 'group' (A or B) and 'score'. The analyst wants to test if the mean scores of the two groups are significantly different. The data is normally distributed and the variances are equal. Which statistical test should be used?

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