Question 482 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is using Amazon SageMaker Data Wrangler for exploratory data analysis. The dataset contains a column with missing values that are encoded as 'NA' strings. The data scientist wants to treat these as missing values during the import. Which step should the data scientist take?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure a custom missing value symbol 'NA' in the import settings of Data Wrangler.
Amazon SageMaker Data Wrangler allows specifying custom missing value symbols during the import step. By configuring 'NA' as a custom missing value symbol in the import settings, Data Wrangler will automatically treat 'NA' strings as missing values when reading the dataset. Option B is incorrect because the 'Impute' transform is used to fill missing values after they have been recognized as missing; it does not handle the initial identification of 'NA' strings as missing. Option C is incorrect because using 'Replace missing' after import is less efficient and not the recommended approach; it is better to handle it during import to ensure downstream transforms treat the values correctly. Option D is incorrect because dropping rows with 'NA' prematurely discards data before any analysis; the goal is to treat 'NA' as missing, not to remove the rows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure a custom missing value symbol 'NA' in the import settings of Data Wrangler.
Why this is correct
Data Wrangler supports custom missing value symbols during data import.
- ✗
Use the 'Impute' transform to fill 'NA' with the mean of the column.
Why it's wrong here
Imputation should be done after missing values are properly recognized.
- ✗
Use the 'Replace missing' transform to replace 'NA' with null after import.
Why it's wrong here
Possible but less efficient; custom missing values during import is better.
- ✗
Use the 'Drop missing' transform to remove rows containing 'NA'.
Why it's wrong here
Drops rows before handling missing values, but 'NA' strings are not recognized as missing by default.
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Last reviewed: Jun 20, 2026
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