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Machine Learning Implementation and Operations →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to train a small, fast model on a random sample of the data using a cheaper instance like ml.m5.xlarge, then use prediction confidence to flag low-confidence examples for manual review. This approach works because it directly addresses the need to identify mislabeled training data without processing the full 50 GB dataset, keeping costs under $50 and runtime under 2 hours by leveraging a lightweight model on a smaller, cost-effective instance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to balance accuracy improvement with strict budget constraints, a common real-world challenge where full retraining or expensive human re-labeling is not feasible. A frequent trap is assuming you must use the same expensive instance or process all data, but the key is efficient sampling and cheap inference. Memory tip: think "Sample, Small, Score" — sample a subset, train a small model, score confidence to spot mislabels.

MLS-C01 Practice Question: Machine Learning Implementation and Operations

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 media company uses Amazon SageMaker to train a deep learning model for video classification. The training job uses a single ml.p3.2xlarge instance and processes 50 GB of labeled video data stored in Amazon S3. The training completes successfully in 12 hours. However, the data scientists report that the model’s accuracy is lower than expected. They suspect the training data contains labeling errors. To improve model accuracy without incurring significant additional cost, they want to identify and remove mislabeled training examples before retraining. They have a small budget of $50 and need to complete the analysis within 2 hours. Which approach should the data scientists take?

Question 1hardmultiple choice

Full question →

A
Use SageMaker Ground Truth to create a new labeling job for the entire dataset, then compare the new labels with the original labels to identify discrepancies.
Why wrong: This would exceed the $50 budget and 2-hour time limit, as Ground Truth labeling for 50 GB of video data is costly and time-consuming.
B
Use SageMaker Clarify to generate a bias report for the training data and remove instances that contribute to bias.
Why wrong: SageMaker Clarify detects bias in features and predictions, but does not directly identify mislabeled examples. This approach may not improve accuracy from labeling errors.
C
Train a small, fast model on a random sample of the data (e.g., 1 GB) using a cheaper instance like ml.m5.xlarge, then use the model's prediction confidence to flag low-confidence examples as potential mislabels for manual review.
This approach is cost-effective (within $50) and fast (under 2 hours). The small model can identify likely mislabeled examples by low confidence, allowing targeted manual review.
D
Manually review all 50 GB of video data to correct labels.
Why wrong: Manual review of 50 GB of video data is not feasible within 2 hours and would far exceed the $50 budget if using human annotators.

Full breakdown with real-world context →

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

✓

Train a small, fast model on a random sample of the data (e.g., 1 GB) using a cheaper instance like ml.m5.xlarge, then use the model's prediction confidence to flag low-confidence examples as potential mislabels for manual review.

Option C is correct because training a small, fast model on a 1 GB random sample using a cheaper instance (ml.m5.xlarge) allows the team to quickly identify low-confidence predictions, which are strong indicators of mislabeled examples. This approach fits within the $50 budget and 2-hour time constraint, as it avoids processing the full 50 GB dataset and leverages a lightweight model for rapid iteration. By flagging only suspicious samples for manual review, the team can efficiently clean the training data without incurring the cost of re-labeling the entire dataset.

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.

✗
Use SageMaker Ground Truth to create a new labeling job for the entire dataset, then compare the new labels with the original labels to identify discrepancies.
Why it's wrong here
This would exceed the $50 budget and 2-hour time limit, as Ground Truth labeling for 50 GB of video data is costly and time-consuming.
✗
Use SageMaker Clarify to generate a bias report for the training data and remove instances that contribute to bias.
Why it's wrong here
SageMaker Clarify detects bias in features and predictions, but does not directly identify mislabeled examples. This approach may not improve accuracy from labeling errors.
✓
Train a small, fast model on a random sample of the data (e.g., 1 GB) using a cheaper instance like ml.m5.xlarge, then use the model's prediction confidence to flag low-confidence examples as potential mislabels for manual review.
Why this is correct
This approach is cost-effective (within $50) and fast (under 2 hours). The small model can identify likely mislabeled examples by low confidence, allowing targeted manual review.
Related concept
Read the scenario before looking for a memorised answer.
✗
Manually review all 50 GB of video data to correct labels.
Why it's wrong here
Manual review of 50 GB of video data is not feasible within 2 hours and would far exceed the $50 budget if using human annotators.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may choose SageMaker Ground Truth (Option A) assuming it is the standard tool for label correction, but they overlook the strict budget and time constraints that make it infeasible for the full dataset.

Detailed technical explanation

How to think about this question

This approach leverages the concept of 'active learning' or 'uncertainty sampling,' where a model's prediction confidence (e.g., softmax probability) is used to flag examples near the decision boundary as likely mislabeled. Training a smaller, faster model (e.g., a lightweight CNN) on a random sample reduces computational cost and time, while the low-confidence heuristic effectively surfaces annotation errors without needing a fully accurate model. In practice, this technique is common in data-centric AI workflows, where cleaning a small, representative subset can yield disproportionate accuracy gains.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

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: Train a small, fast model on a random sample of the data (e.g., 1 GB) using a cheaper instance like ml.m5.xlarge, then use the model's prediction confidence to flag low-confidence examples as potential mislabels for manual review. — Option C is correct because training a small, fast model on a 1 GB random sample using a cheaper instance (ml.m5.xlarge) allows the team to quickly identify low-confidence predictions, which are strong indicators of mislabeled examples. This approach fits within the $50 budget and 2-hour time constraint, as it avoids processing the full 50 GB dataset and leverages a lightweight model for rapid iteration. By flagging only suspicious samples for manual review, the team can efficiently clean the training data without incurring the cost of re-labeling the entire dataset.

What should I do if I get this MLS-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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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.