Screen on evidence, not on a CV
9 question types in one paper: objective questions scored on submission, coding problems executed against your test cases, and long-form answers your team reviews against a rubric.
- Mixed sections
- Weighting you control
- Question-level analytics
Design the round
One paper, several kinds of evidence
Knowledge, reasoning and implementation ability are different things. A screening round should be able to measure more than one of them.
Mix question types in one paper
A screening round rarely wants only one kind of question. Sections can be objective, coding or both, each weighted the way you decide.
Sections that mirror your process
Fundamentals, then applied reasoning, then code. Each section carries its own question set and contributes its own share of the score.
Weighting you control
Decide what a section is worth. A role where correctness matters more than breadth should score that way, not by accident of question count.
Automatic where it should be
Objective and coding questions are scored on submission. Nobody marks multiple choice by hand, and nobody should pretend prose can be marked without reading it.
Human where it must be
Comprehension, long answer and case study responses go to a review queue with your rubric. The result records who scored it.
Question-level analytics
Difficulty and discrimination per question across the cohort, so you can tell a genuinely hard question from a badly worded one.
Process
From role definition to shortlist
- 01Step
Define what the role needs
Pick the topics and difficulty mix. Tags and categories in the question bank make this a filter rather than a rewrite.
- 02Step
Assemble the paper
Add sections, pull questions or draw from a randomising pool, set duration and weighting, and preview it as a candidate would see it.
- 03Step
Screen the pipeline
Invite candidates as they apply. Objective and coding scores land automatically; long-form answers queue for review.
- 04Step
Compare on the same basis
Every candidate answered a comparable paper under comparable conditions, with integrity signals attached to each attempt.
Scoring
Automatic where it is honest to be
Objective questions and code have a correct answer a machine can check. Prose does not, and scoring it automatically would produce a number nobody should act on.
- Objective and coding scores are available the moment an attempt is submitted
- Long-form answers queue for a reviewer with your rubric alongside
- The result records which questions were machine-scored and who marked the rest
- A candidate's total is never a mix of measured and guessed
Scored automatically
- Single choice
- Multiple choice
- Fill in the blank
- Image based
- Code snippet MCQ
- Coding problem
Scored by a reviewer
- Paragraph / comprehension
- Subjective / long answer
- Case study
Fair comparison
Make candidates comparable, not identical
Randomised pools give each candidate a different paper drawn from the same specification — comparable in difficulty and coverage without being the same questions.
- Specify the pool and how many questions to draw
- Filter a pool by topic and difficulty so the draw stays balanced
- Sharing questions between candidates stops being useful
- Cohort analytics still compare cleanly, because the specification is shared
Same specification
Five medium-difficulty questions on data structures means the same thing for every candidate, even when the five differ.
Comparable results
Per-question analytics show whether a pool is drawing evenly, so a candidate is not penalised for an unluckily hard draw.
Questions
What hiring teams ask
Can one assessment contain both MCQ and coding?
How do you stop a screening round from just filtering for test-taking skill?
Do you generate an assessment from a job description?
Can different interviewers see different assessments?
How long does it take to set up a first screen?
Build your first screening round
Free trial, no card. Bring one role and see whether the round tells you something a CV did not.
Have questions? Email sales@parikshafy.com