AIF-C01 Fundamentals of AI and ML Practice Question
A data engineer is using Amazon SageMaker Data Wrangler to prepare tabular data for ML. Which THREE data transformations are natively supported? (Choose three.)
⚠ Common exam trap
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.
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
✓
One-hot encoding for categorical features
SageMaker Data Wrangler natively supports one-hot encoding as a categorical-encoding transform, which converts categorical features into binary indicator columns suitable for ML models, so option A is correct. It also provides a text vectorization transform using TF-IDF (and other text transforms like bag-of-words and n-gram) to convert text fields into numeric feature vectors, making option C correct. Data Wrangler additionally allows custom transformations through the Custom Transform node, where users can write their own Pandas or PySpark code, so option D is correct. Options B and E are not native Data Wrangler tabular transforms: audio feature extraction and image resizing/normalization are handled by other AWS services or frameworks (for example, SageMaker Processing with librosa or image libraries), not by Data Wrangler's built-in transform list.
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
Data Wrangler includes a built-in one-hot encoding transform that converts categorical columns into binary indicator features, selectable directly in the transformation list. It satisfies the native transformation requirement without custom code, unlike techniques requiring external libraries or manual scripting.
- ✗
Audio feature extraction
Why it's wrong here
Data Wrangler's transform list covers tabular operations such as handling missing values, encoding categoricals and scaling numerics; audio feature extraction belongs to signal-processing pipelines, not tabular preparation. It would be tempting when building speech models, where dedicated audio tooling is required instead.
- ✓
Text vectorization using TF-IDF
Why this is correct
Data Wrangler provides a native TF-IDF transform that vectorises text columns into weighted numeric features, configurable through the interface. This satisfies the native transformation requirement for text data, producing sparse vectors suitable for downstream ML without writing custom code.
- ✓
Custom Python code via Pandas or Spark
Why this is correct
Data Wrangler supports a custom transform that runs user-supplied Python using Pandas or PySpark on the dataset. This satisfies the native transformation requirement by allowing arbitrary feature engineering within the flow, rather than restricting users to pre-built transforms only.
- ✗
Image resizing and normalization
Why it's wrong here
Data Wrangler transforms operate on tabular rows and columns; image resizing and normalisation are computer-vision preprocessing steps performed by frameworks such as Amazon Rekognition or custom pipelines. It tempts when preparing image datasets, where those operations are genuinely needed, but not for tabular ML data.
Go deeper
Related to this question
About these practice questions
Courseiva writes every AIF-C01 question from scratch — 862 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.