A data analyst has a DataFrame with a 'response_time' column that contains some negative values due to data entry errors. They want to replace these negative values with the median of the positive values. Which steps are correct?

median = df.loc[df['response_time'] > 0, 'response_time'].median(); df.loc[df['response_time'] < 0, 'response_time'] = median

df['response_time'].fillna(df['response_time'].median())

df['response_time'] = df['response_time'].abs()

df['response_time'] = df['response_time'].clip(lower=0)

A data engineer has a large CSV file (5 GB) that does not fit into memory. They need to compute the mean of a numeric column and the count of unique values in another column. Which approach is most memory-efficient?

Convert the CSV to a SQLite database and query it

Load the entire file with pd.read_csv() and then compute statistics

Use pd.read_csv(..., nrows=100000) to sample the data

Process the file in chunks using pd.read_csv(..., chunksize=10000) and accumulate results

A data analyst has a DataFrame with a 'score' column that has some missing values. They need to compute the 90th percentile of the score. Which approach is most appropriate?

df['score'].dropna().quantile(0.9)

df['score'].fillna(df['score'].mean()).quantile(0.9)

df['score'].fillna(0).quantile(0.9)

GIAC Python Coder

Domain 4Objective 1

Data Analysis with Python GPYC Practice Questions (Page 3)

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 11–15

11expert · hard
A data analyst is analyzing a dataset of login times and wants to test whether the mean login time differs between two user groups. The data is not normally distributed and the sample sizes are small. Which statistical test is most appropriate?
Select an answer first
12application · medium
A data analyst has a DataFrame with a 'response_time' column that contains some negative values due to data entry errors. They want to replace these negative values with the median of the positive values. Which steps are correct?
Select an answer first
13expert · hard
A data engineer has a large CSV file (5 GB) that does not fit into memory. They need to compute the mean of a numeric column and the count of unique values in another column. Which approach is most memory-efficient?
Select an answer first
14expert · hard
A data analyst has a DataFrame with a 'score' column that has some missing values. They need to compute the 90th percentile of the score. Which approach is most appropriate?
Select an answer first
15foundation · easy
Which pandas function reads an Excel workbook and returns a DataFrame?
Select an answer first

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Data Analysis with Python GPYC Practice Questions (Page 3)

Questions 11–15

A data analyst is analyzing a dataset of login times and wants to test whether the mean login time differs between two user groups. The data is not normally distributed and the sample sizes are small. Which statistical test is most appropriate?

A data analyst has a DataFrame with a 'response_time' column that contains some negative values due to data entry errors. They want to replace these negative values with the median of the positive values. Which steps are correct?

A data engineer has a large CSV file (5 GB) that does not fit into memory. They need to compute the mean of a numeric column and the count of unique values in another column. Which approach is most memory-efficient?

A data analyst has a DataFrame with a 'score' column that has some missing values. They need to compute the 90th percentile of the score. Which approach is most appropriate?

Which pandas function reads an Excel workbook and returns a DataFrame?

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