easyMultiple Select
MLA-C01 Practice Question: A team uses SageMaker Ground Truth to create…
A team uses SageMaker Ground Truth to create labeled datasets. They need to ensure labeling jobs are cost-effective. Which TWO measures should they take? (Select TWO.)
⚠ Common exam trap
AWS often tests the misconception that reducing compute instance size or workforce type directly lowers labeling costs, when in reality, Ground Truth costs are driven by the number of human annotations and the use of automated labeling features.
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
✓
Set up a labeling workflow with 'Incremental training'.
Option C is correct because SageMaker Ground Truth supports incremental training, which reuses labels and model artifacts from previous labeling jobs so that the auto-labeling model starts from an already-trained state, reducing the amount of human labeling and therefore cost on subsequent jobs. Option E is correct because Automated data labeling (active learning) uses a machine learning model to label a portion of the dataset automatically and only sends low-confidence items to human workers, which lowers the number of human annotations billed. Option A is not correct because instance type affects throughput and speed, not the fundamental per-label cost, and Ground Truth manages the labeling instances. Option B is not correct because workforce type (public, private, vendor) changes who labels and the price per label, but choosing a 'smaller' workforce type is not a defined cost-optimization measure and does not inherently reduce cost. Option D is not correct because Consolidated billing is an AWS Organizations billing feature for aggregating charges across accounts, not a Ground Truth labeling cost-reduction mechanism.
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 a smaller instance type for the labeling job.
Why it's wrong here
Ground Truth labelling jobs run on the service's managed infrastructure, so instance type is not a configurable cost lever for the labelling task itself. It is tempting because instance sizing drives cost in ordinary SageMaker training and inference jobs, but not here.
- ✗
Use a smaller workforce type.
Why it's wrong here
Workforce type selects who labels data, such as public or private workers, and does not by itself reduce cost; private pricing can exceed public. It is tempting because workforce choice feels cost-relevant, but the correct measures are automated labelling and reduced manual review volume.
- ✓
Set up a labeling workflow with 'Incremental training'.
Why this is correct
Incremental training reuses labels from earlier Ground Truth jobs to pre-annotate subsequent data, reducing human labelling effort and cost. It suits iterative datasets where prior work informs new batches, directly serving the stem's cost-effectiveness requirement.
- ✗
Enable the 'Consolidated billing' for labeling costs.
Why it's wrong here
Consolidated billing aggregates AWS charges across accounts for invoicing; it does not lower Ground Truth labelling costs. It is tempting because it sounds like a cost-control feature, but it is an account-structure setting, whereas the correct measures cut the number of human-labelled items.
- ✓
Use the 'Automated data labeling' feature.
Why this is correct
Automated data labelling uses active learning to label a subset automatically and only sends low-confidence items to human annotators, cutting the volume of manual labelling work. This directly reduces Ground Truth labelling costs, satisfying the cost-effectiveness constraint in the stem.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.