AI Data Evaluator
Deliverable:
Annotated Data Queue
Early Career
30 Minutes
SKILLS COVERED
RELEVANT ROLES
About This Test
Scenario
As an AI Data Evaluator, you ensure the accuracy of AI training by meticulously analyzing and annotating diverse datasets. Your role is to evaluate each piece of data to ensure it meets the quality standards needed for AI training, guided by detailed annotation guidelines.
Task Instructions:
Review Annotation Guidelines: Familiarize yourself with the annotation guidelines provided. These will instruct you on how to accurately process various types of data, including text, images, and audio clips.
Annotate Data: You will handle a small queue containing different data types. Your task is to assess and annotate each item according to the guidelines within the allotted time. You will be prompted to record your annotation decisions for each piece of data.
Exercise Judgment: When you encounter data that does not clearly fit into predefined categories, document your decision-making process. Note what additional context or information might help clarify how to annotate this data effectively.
Why We Chose It
This task is specifically designed to mimic the real-world challenges that AI data evaluators frequently encounter.
It tests candidates on interpretation and application of annotation guidelines, effective time management, and capacity to make sound decisions in ambiguous situations.
Assign This Test
How It Works
Select a test and set customization preferences
Preview your custom test
SkillsTrust handles test delivery and preliminary scoring
Review your scored submissions and progress candidates
Customization
Each test comes with reference material (ex. a dataset, financial statement, report etc) which candidates use to complete their task.
Reference material is fully customized to match your industry, company size, and business context.
Scoring
Each work sample submission is scored using a detailed skills-based rubric.
What You'll Learn About Candidates
Judgment: Ability to make informed decisions based on the annotation guidelines.
Efficiency: Speed and accuracy in processing data under time constraints.
Communication: Skill in documenting annotation decisions, especially when data is unclear.