The answer is three natively supported transformations in SageMaker Data Wrangler include one-hot encoding, custom Python code via Pandas or Spark, and handling missing values. These are correct because Data Wrangler provides a visual interface with over 300 built-in transformations, including one-hot encoding for converting categorical data into binary columns, custom code blocks for flexible preprocessing using Pandas or PySpark, and imputation or drop operations for missing data—all essential for preparing tabular data for machine learning. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your familiarity with Data Wrangler’s native capabilities versus external tools; a common trap is assuming only basic operations like scaling are supported, but the service emphasizes both automated and custom transformations. Remember the memory tip: “One-hot, custom code, and missing—three native tricks for data fixing.”
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 Amazon SageMaker Data Wrangler to prepare tabular data for ML. Which THREE data transformations are natively supported? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
One-hot encoding for categorical features
Option A is correct because Amazon SageMaker Data Wrangler includes built-in support for one-hot encoding as a native transformation for categorical features. This transformation automatically creates binary columns for each category, which is essential for preparing tabular data for machine learning models that require numerical input.
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.
✓
One-hot encoding for categorical features
Why this is correct
One-hot encoding is a built-in transform in Data Wrangler.
Related concept
Read the scenario before looking for a memorised answer.
✗
Audio feature extraction
Why it's wrong here
Audio processing is not a built-in transform in Data Wrangler.
✓
Text vectorization using TF-IDF
Why this is correct
Data Wrangler includes text vectorizers like TF-IDF and count vectorizer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Custom Python code via Pandas or Spark
Why this is correct
Data Wrangler allows custom transformations using Python code.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image resizing and normalization
Why it's wrong here
Image transformations are not natively supported; Data Wrangler focuses on tabular and text data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between natively supported transformations in SageMaker Data Wrangler versus those requiring external services or custom scripts, leading candidates to mistakenly select audio or image processing options that are not part of Data Wrangler's built-in capabilities.
Detailed technical explanation
How to think about this question
SageMaker Data Wrangler natively supports over 300 built-in transformations, including one-hot encoding, text vectorization via TF-IDF, and custom Python code using Pandas or Spark. Under the hood, Data Wrangler uses Apache Spark for distributed data processing, allowing transformations to scale efficiently. A real-world scenario is preparing a customer churn dataset where one-hot encoding converts 'Region' into binary columns, TF-IDF vectorizes 'Feedback' text, and custom Python code handles outlier removal—all within a single visual interface.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: One-hot encoding for categorical features — Option A is correct because Amazon SageMaker Data Wrangler includes built-in support for one-hot encoding as a native transformation for categorical features. This transformation automatically creates binary columns for each category, which is essential for preparing tabular data for machine learning models that require numerical input.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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