The answer is the Handle rare values transform. This is correct because SageMaker Data Wrangler’s Handle rare values transform is purpose-built for grouping rare categories in a categorical column, allowing you to set a frequency threshold—such as 1%—so that any category appearing less often is automatically consolidated into an ‘Other’ bucket, directly reducing dimensionality without manual mapping. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of built-in Data Wrangler transforms versus custom coding; a common trap is confusing this with the Handle missing values transform or assuming you need a Lambda function. Remember, when you see a need for grouping rare categories data wrangler tasks with a frequency cutoff, think “Handle rare values” as your one-click solution. Memory tip: “Rare” is right in the name—Handle rare values handles the rare ones.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 scientist is using Amazon SageMaker Data Wrangler for feature engineering on a large dataset stored in S3. The dataset has a column 'ProductCategory' with 1000+ unique values. To reduce dimensionality, they want to group categories that appear less than 1% of the time into an 'Other' category. Which Data Wrangler transform should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Handle rare values
The 'Handle rare values' transform in SageMaker Data Wrangler is specifically designed to group infrequent category values into a single 'Other' bucket based on a frequency threshold (e.g., less than 1%). This directly addresses the need to reduce dimensionality by consolidating rare categories without requiring custom code or manual grouping.
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.
✗
Group similar categories
Why it's wrong here
This transform requires manual mapping, not automatic threshold-based grouping.
✗
Custom transform with Python
Why it's wrong here
While possible, it is less efficient than using the built-in transform.
✓
Handle rare values
Why this is correct
This built-in transform can group categories below a frequency threshold into an 'Other' value.
Related concept
Read the scenario before looking for a memorised answer.
✗
One-hot encode with threshold
Why it's wrong here
One-hot encoding with a threshold creates dummy variables, not grouping into 'Other'.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the 'Handle rare values' transform with the 'One-hot encode with threshold' transform, mistakenly thinking the threshold in one-hot encoding serves the same purpose as grouping rare categories, when in fact it limits the number of one-hot columns created, not the grouping of infrequent values.
Detailed technical explanation
How to think about this question
Under the hood, the 'Handle rare values' transform in Data Wrangler computes the frequency distribution of each category and then replaces any category whose relative frequency falls below the specified threshold (e.g., 0.01 for 1%) with a single placeholder value (default 'Other'). This is a common preprocessing step to avoid the curse of dimensionality in high-cardinality categorical features, and it integrates seamlessly with downstream SageMaker pipelines without requiring custom code.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Handle rare values — The 'Handle rare values' transform in SageMaker Data Wrangler is specifically designed to group infrequent category values into a single 'Other' bucket based on a frequency threshold (e.g., less than 1%). This directly addresses the need to reduce dimensionality by consolidating rare categories without requiring custom code or manual grouping.
What should I do if I get this MLA-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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This MLA-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 MLA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.