AI in Resume Parsing and Talent Matching Quiz

Answers and explanations appear as you take the quiz.

1. What does resume parsing primarily do?
   • Converts unstructured resume text into structured fields like skills and dates
   • Writes resumes for candidates
   • Interviews candidates by phone
   • Sets the salary for each role
2. How does modern semantic matching improve on old keyword filters?
   • It only looks for exact word matches
   • It understands related terms, so 'React developer' can match 'front-end engineer'
   • It ignores the job description
   • It requires candidates to retype their resume
3. Why is bias a serious concern in AI talent matching?
   • AI cannot read PDFs
   • Resumes are always anonymous
   • Models trained on historical hiring can learn and amplify past discrimination
   • Matching is purely random
4. Which technique converts text into numerical vectors for similarity comparison?
   • OCR scanning
   • Spreadsheet sorting
   • Spell check
   • Embeddings from transformer models
5. What might be needed to extract text from a scanned, image-based resume?
   • Optical character recognition (OCR)
   • A louder microphone
   • A faster printer
   • A new job posting
6. What is one possible use of semantic matching in a resume-screening workflow?
   • Automatically decide who should be hired without human review
   • Compare job requirements with skills described in candidate profiles
   • Guarantee a fair outcome for every applicant
   • Infer protected traits from a candidate’s name
7. When can an OCR step be useful in resume parsing?
   • When comparing two already-computed embedding vectors
   • When deciding a candidate’s interview score
   • When a resume is a scanned image and its text is not directly selectable
   • When converting a job title into a salary
8. What does cosine similarity compare in the guide’s embedding-based matcher?
   • The number of pages in each document
   • The chronological order of work history entries
   • The acoustic pitch of candidate names
   • The direction or angular similarity of job-description and candidate-profile vectors