The answer is that the crawler sample size is too small, and the sampled rows contain non-numeric values. AWS Glue crawlers infer schema by sampling the first few rows of a dataset—by default, only the first two megabytes—so if those initial rows include headers, nulls, or text entries, the crawler conservatively assigns the 'string' type to all columns, even when the bulk of the data contains numeric values. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how crawler sampling thresholds and data quality at the top of a file directly impact schema inference, a common pitfall when working with wide datasets of 200+ columns. The trap is assuming that column names or file formats like Parquet or JSON guarantee correct typing, but the crawler’s behavior is purely row-based and sample-dependent. Memory tip: think “first few rows fool the crawler”—if your sample is dirty or small, strings will swallow numbers.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Option D is correct because the crawler samples data and may not see enough numeric values if the sample size is small or if the first few rows contain non-numeric values (e.g., headers or missing values). Option A is incorrect because the crawler does not rely on column names for type inference. Option B is incorrect because Parquet files store schema, but if the data is CSV, the crawler infers types. Option C is incorrect because JSON files also have type information, but the crawler can still infer incorrectly.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The crawler sample size is too small, and the sampled rows contain non-numeric values. — Option D is correct because the crawler samples data and may not see enough numeric values if the sample size is small or if the first few rows contain non-numeric values (e.g., headers or missing values). Option A is incorrect because the crawler does not rely on column names for type inference. Option B is incorrect because Parquet files store schema, but if the data is CSV, the crawler infers types. Option C is incorrect because JSON files also have type information, but the crawler can still infer incorrectly.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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