Free Practice Quiz Question List

8 End-to-End Data Analysis Project Online Quiz Questions

Use this free practice quiz with 20 questions to review 8 End-to-End Data Analysis Project, test your knowledge, and prepare for your next test or exam.

20 questions
01
True or false
1 point

The regression results from the synthetic customer example establish findings about real customers.

  1. A

    True

  2. B

    False

02
Choose one
1 point

An analyst receives a table and wants to interpret its rows correctly before choosing a target or features. Which project-planning question should they answer first?

  1. A

    The target

  2. B

    The unit of observation

  3. C

    The success criteria

  4. D

    The data type

03
Written response
1 point

A numeric variable has a strongly skewed distribution with a few extreme values. Which measure of central tendency is often more representative than the mean?

04
Fill in the blank
1 point

A field is missing because its value may not have been recorded or may not apply. Do not automatically replace it with .

05
Choose one
1 point

A value in an age column is the text unknown. When parsing the column numerically using the example's explicit rule, what should happen to that entry?

  1. A

    Treat the text as an age of zero

  2. B

    Keep the text unchanged as a numeric value

  3. C

    Convert the unparseable entry to a missing value

  4. D

    Delete every row containing an age

06
True or false
1 point

A scatter plot shows that income and spending tend to rise together. By itself, this pattern establishes that higher income causes higher spending.

  1. A

    True

  2. B

    False

07
Choose all
1 point

An analyst is choosing plots for exploratory data analysis. Which statements correctly match a plot type to its use? Select all correct choices.

  1. A

    Use a histogram to examine the distribution of a numeric variable.

  2. B

    Use a histogram to compare counts across named categories.

  3. C

    Use a bar chart to compare counts across categories.

  4. D

    Use a bar chart to show the relationship between two numeric variables.

08
Written response
1 point

Which evaluation metric measures average absolute prediction error in the target's units?

09
Fill in the blank
1 point

To avoid an error when the encoder encounters a previously unseen category, the example configures OneHotEncoder with .

10
Choose one
1 point

A customer dataset has numeric features age and income and a categorical feature region. Which preprocessing plan matches the example?

  1. A

    Numeric features: most-frequent imputation and one-hot encoding; categorical feature: median imputation and standardization

  2. B

    Numeric features: median imputation and standardization; categorical feature: most-frequent imputation and one-hot encoding

  3. C

    All features: median imputation and standardization only

  4. D

    Numeric features: one-hot encoding; categorical feature: standardization only

11
Choose all
1 point

A team wants others to rerun its analysis and inspect its outputs. Which project practices support that goal? Select all correct choices.

  1. A

    Keep the original input unchanged.

  2. B

    Save only cleaned data and discard the original input.

  3. C

    Record the software versions used.

  4. D

    Save figures rather than relying only on notebook display.

12
Choose one
1 point

A model will be used to predict future outcomes from time-ordered data. Which evaluation approach best reflects that intended use?

  1. A

    Use a random split because it is always representative.

  2. B

    Use the same random split for every time-ordered prediction task.

  3. C

    Evaluate only on training data to match the available history.

  4. D

    Choose a split that reflects predicting future observations.

13
Open ended
1 point

A regression model is evaluated on held-out data and does not meaningfully outperform a simple baseline that predicts the training-set mean. Explain how the team should interpret this result and what it should reconsider, while preserving a sound final evaluation.

14
True or false
1 point

A missing value should not automatically be replaced with zero, because it may mean “not recorded” or “not applicable.”

  1. A

    True

  2. B

    False

15
Choose one
1 point

A numeric age field contains the text value “unknown.” If the cleaning step parses values numerically and coerces invalid text, what should happen to that entry?

  1. A

    Treat “unknown” as the age zero.

  2. B

    Convert “unknown” to a missing value because it cannot be parsed as a number.

  3. C

    Keep “unknown” as a valid numeric age.

  4. D

    Replace every age with the column maximum.

16
Choose one
1 point

A numeric feature has a strongly skewed distribution with a few extreme values. Which summary is often more representative of a typical observation?

  1. A

    Use the mean because extreme values cannot affect it.

  2. B

    Use the minimum because it is the least affected by skew.

  3. C

    Consider the median because extreme values can pull the mean.

  4. D

    Use the maximum because it summarizes the typical value.

17
Written response
1 point

After removing surrounding whitespace from region labels, which string method is used to standardize their capitalization?

18
Choose one
1 point

A scatter plot shows an association between income and spending. Which conclusion is justified by that plot alone?

  1. A

    The plotted relationship proves that income causes spending to change.

  2. B

    The plot may show an association, but it does not by itself prove causation.

  3. C

    A scatter plot proves that the variables are unrelated.

  4. D

    The relationship must be caused by the region category.

19
Written response
1 point

Which metric measures average absolute prediction error in the target’s units?

20
Choose one
1 point

A dataset has numeric features that need median imputation and scaling, plus a categorical feature that needs imputation and one-hot encoding. Why is a column transformer useful?

  1. A

    It ensures every column is converted to text before modeling.

  2. B

    It removes the need to inspect missing values.

  3. C

    It applies different preprocessing steps to different types of columns.

  4. D

    It makes the test set part of the training set.