MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is using AWS Glue to catalog a dataset with 200 columns. During exploratory data analysis, they run a crawler and then view the table schema in the AWS Glue Data Catalog. They notice that many columns are inferred as 'string' even though they contain numeric values. What is the most likely cause?
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
✓
The crawler sample size is too small, and the sampled rows contain non-numeric values.
The AWS Glue crawler samples a subset of rows to infer schema. If the sample size is too small or the sampled rows contain non-numeric values (e.g., headers, missing data, or text entries), the crawler may default to 'string' type for columns that are actually numeric. Options A, C, and D are incorrect: JSON files can contain numeric types, Parquet files support numeric types, and special characters in column names do not affect type inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The data is stored in JSON format, which only supports string types.
Why it's wrong here
JSON supports numeric types.
- ✓
The crawler sample size is too small, and the sampled rows contain non-numeric values.
Why this is correct
The crawler samples a subset; if the sample includes non-numeric values, it infers string.
- ✗
The data is stored in Parquet format, which does not support numeric types.
Why it's wrong here
Parquet supports numeric types.
- ✗
The column names contain special characters that prevent type inference.
Why it's wrong here
Column names do not affect type inference.
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Written by Johnson Ajibi, MSc IT Security
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