A data analyst has a DataFrame with a 'timestamp' column in UTC and a 'local_time' column in various time zones. The analyst needs to compute the time difference between the two columns for each row. The 'local_time' column contains timezone-aware strings. Which operation should be used?

Convert 'timestamp' to timezone-aware using tz_localize('UTC'), then subtract 'local_time' after converting it to datetime with tz_convert.

Subtract the two columns directly after converting both to datetime without timezone handling.

Use pd.to_datetime on both columns and then subtract, assuming pandas handles timezones automatically.

Convert 'local_time' to UTC using tz_convert('UTC') and subtract from 'timestamp' after converting 'timestamp' to datetime.

A security analyst wants to compare the distribution of packet sizes between two different protocols (TCP and UDP) in a single plot. Which visualization is most appropriate?

Overlaid histograms with transparency

A scatter plot of packet size vs. time

A pie chart of protocol proportions

A single line plot of the mean packet size

A data engineer needs to load a large JSON file that contains a nested array of objects. They want to flatten it into a DataFrame where each object in the array becomes a row, and they also need to handle missing keys gracefully. Which approach is most robust?

Use pd.DataFrame.from_dict with orient='index'

Use pd.read_json with orient='records'

Use a list comprehension to extract fields manually

Use pd.json_normalize with record_path and errors='ignore'

GIAC Python Coder

Domain 4Objective 1

Data Analysis with Python GPYC Practice Questions (Page 4)

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)

33questions here

7free pages

5concepts

Questions 16–20

16expert · hard
A data analyst has a DataFrame with a 'timestamp' column in UTC and a 'local_time' column in various time zones. The analyst needs to compute the time difference between the two columns for each row. The 'local_time' column contains timezone-aware strings. Which operation should be used?
Select an answer first
17foundation · easy
Which Seaborn function is used to create a histogram with a kernel density estimate (KDE) overlay?
Select an answer first
18application · medium
A security analyst wants to compare the distribution of packet sizes between two different protocols (TCP and UDP) in a single plot. Which visualization is most appropriate?
Select an answer first
19application · medium
A data analyst has a DataFrame with a 'score' column that contains some outliers. The analyst wants to compute a robust measure of central tendency that is not heavily influenced by outliers. Which statistic should be used?
Select an answer first
20expert · hard
A data engineer needs to load a large JSON file that contains a nested array of objects. They want to flatten it into a DataFrame where each object in the array becomes a row, and they also need to handle missing keys gracefully. Which approach is most robust?
Select an answer first

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Data Analysis with Python GPYC Practice Questions (Page 4)

Questions 16–20

A data analyst has a DataFrame with a 'timestamp' column in UTC and a 'local_time' column in various time zones. The analyst needs to compute the time difference between the two columns for each row. The 'local_time' column contains timezone-aware strings. Which operation should be used?

Which Seaborn function is used to create a histogram with a kernel density estimate (KDE) overlay?

A security analyst wants to compare the distribution of packet sizes between two different protocols (TCP and UDP) in a single plot. Which visualization is most appropriate?

A data analyst has a DataFrame with a 'score' column that contains some outliers. The analyst wants to compute a robust measure of central tendency that is not heavily influenced by outliers. Which statistic should be used?

A data engineer needs to load a large JSON file that contains a nested array of objects. They want to flatten it into a DataFrame where each object in the array becomes a row, and they also need to handle missing keys gracefully. Which approach is most robust?

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