The correct answer is to specify the objective metric as 'F1' or 'AUC' when configuring the SageMaker Autopilot job. This works because these metrics are threshold-independent and focus on the trade-off between precision and recall or true positive and false positive rates, making them inherently robust to severe class imbalance where accuracy would be misleading. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that Autopilot does not support manual data-level techniques like undersampling or SMOTE, nor does it expose a direct class-weight parameter; instead, it relies on the objective metric to guide model optimization. A common trap is assuming you must preprocess the data manually, but Autopilot’s built-in algorithms can handle imbalance when given the right objective. Memory tip: For imbalanced binary classification, remember “F1 or AUC, not accuracy” to avoid the 99% accuracy trap.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
An ML team is using SageMaker Autopilot to automatically build a binary classification model. The dataset has 500,000 rows and 200 columns, with a severe class imbalance (1% positive). Which configuration should the team set to address the imbalance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Specify the 'objective' as 'F1' or 'AUC' to optimize for imbalanced data.
SageMaker Autopilot's AutoML job allows specifying objective metric such as F1 or AUC for imbalanced data (Option D). Option A (undersampling) is not built into Autopilot. Option B (class weights) is not directly configurable. Option C (SMOTE) is not supported by Autopilot.
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.
✓
Specify the 'objective' as 'F1' or 'AUC' to optimize for imbalanced data.
Why this is correct
F1 and AUC are better metrics for imbalanced classification.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set the 'problem_type' to 'MulticlassClassification' to handle imbalance.
Why it's wrong here
The problem type is binary; multiclass is incorrect.
✗
Use the 'AutoML' job with 'EnsembleMode' and 'SMOTE' sampling.
Why it's wrong here
SMOTE is not a built-in option in Autopilot.
✗
Configure the data split to use stratified sampling based on the target.
Why it's wrong here
Stratification helps validation but does not address imbalance in training.
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 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 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.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Specify the 'objective' as 'F1' or 'AUC' to optimize for imbalanced data. — SageMaker Autopilot's AutoML job allows specifying objective metric such as F1 or AUC for imbalanced data (Option D). Option A (undersampling) is not built into Autopilot. Option B (class weights) is not directly configurable. Option C (SMOTE) is not supported by Autopilot.
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.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is using SageMaker Autopilot to automatically build a classification model. The dataset is highly imbalanced (1% positive class). Which configuration should the scientist set to handle the class imbalance?
hard
A.Set the problem_type to 'BinaryClassification' and enable 'balance_class_weights'.
✓ B.Use the 'AutoMLJobObjective' with 'F1' metric.
C.Set the 'sample_weight' attribute in the input data.
D.Manually downsample the majority class before training.
Why B: SageMaker Autopilot does not directly handle class imbalance automatically. The user can specify a 'problem_type' and 'target_attribute_name', but to address imbalance, they should enable oversampling or use custom recipes. However, among the options, setting the objective metric to 'F1' or 'AUC' is a common technique, but Autopilot allows setting 'balance_class_weights' to 'True' or using 'AutoMLJobObjective' with 'F1'. The correct answer is to use the 'AutoMLJobObjective' with 'F1' metric, as Autopilot will focus on optimizing F1, which is sensitive to imbalance.
Last reviewed: Jun 20, 2026
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This MLS-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 MLS-C01 exam.
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