Question 996 of 619
SageMaker Ground Truth Capabilities: Workflows, Workforce & Active Learning
Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)
Quick Answer
The answer is that Amazon SageMaker Ground Truth integrates with Amazon SageMaker to use the labeled data for training, which is a core capability tested on the AWS Certified AI Practitioner AIF-C01 exam. This is correct because Ground Truth is designed as a data labeling service that feeds directly into SageMaker’s model training pipelines; once a labeling job is complete, the output dataset is automatically stored in an S3 bucket and can be immediately referenced by a SageMaker training job without manual data transfer. On the exam, this question tests your understanding of how Ground Truth’s built-in workflows—such as those for image classification and object detection—simplify the labeling process by providing pre-built UI templates, while a common trap is confusing Ground Truth with a standalone labeling tool that does not integrate with SageMaker. To remember this, think of Ground Truth as the “labeling engine” that powers SageMaker’s training: it creates the high-quality labeled data that SageMaker consumes, not just a separate annotation service.
⚠ Common exam trap
AWS often tests the misconception that Ground Truth is limited to text data or only supports public workforces, while in reality it handles multiple data modalities and offers flexible workforce options including private and vendor-managed.
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
✓
It provides built-in workflows for image classification and object detection.
Amazon SageMaker Ground Truth provides built-in workflows for common tasks like image classification and object detection, which simplifies the setup of labeling jobs. These pre-built templates handle the UI and data formatting, allowing users to focus on the labeling instructions rather than building the labeling interface from scratch.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It can only be used for text data.
Why it's wrong here
It supports image, video, and text.
- ✓
It provides built-in workflows for image classification and object detection.
Why this is correct
Ground Truth supports these tasks.
- ✓
It supports automated data labeling using active learning.
Why this is correct
Active learning reduces manual labeling effort.
- ✓
It integrates with Amazon SageMaker to use the labeled data for training.
Why this is correct
Labeled data can be exported to S3 and used in SageMaker training.
- ✗
It can only use a public workforce from Amazon Mechanical Turk.
Why it's wrong here
It also supports private workforces (Vendors and your own employees).
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Same concept, more angles
1 more way this is tested on AIF-C01
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 company wants to use Amazon SageMaker Ground Truth to build a labeled dataset for a custom object detection model. Which TWO labeling strategies are available? (Choose two.)
medium- ✓ A.Private workforce labeling (company employees)
- ✓ B.Crowd-based labeling using Amazon Mechanical Turk
- C.Automated labeling using pre-trained models
- D.Active learning with manual verification
- E.Fully automated labeling via AWS Lambda
Why A: Amazon SageMaker Ground Truth supports private workforce labeling where company employees (e.g., via a corporate directory or invited users) perform manual annotation. This is ideal for sensitive data or domain-specific tasks like custom object detection, where internal expertise ensures high label accuracy.
Last reviewed: Jun 30, 2026
This AIF-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 AIF-C01 exam.
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