MLS-C01 Practice Question: Machine Learning Implementation and Operations
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?
⚠ Common exam trap
Many candidates 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.
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.
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.
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.
- ✗
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.