Prompt Engineer Assessment Test

This Prompt Engineering test evaluates candidates' ability to design, refine, and optimize prompts for AI models, ensuring effective communication and desired outcomes. It assesses understanding of programming logic and problem-solving skills essential for generative AI applications.

Proficiency Level

Beginner-Expert

Experience

0-8 years

Duration

60 mins

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WeCP

Subject Matter Expert

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Use Case

Assesses understanding of fundamental prompting techniques for language models.
Evaluates ability to enhance clarity and creativity in prompts.
Tests skills in writing code and handling AI models technically.
Identifies top talent through complex prompt design challenges.

Skills Covered

Prompt Engineering

Programming Logic

About

Prompt Engineer Assessment Test

This Prompt Engineering test is designed to assess the candidate's proficiency in crafting and optimizing prompts for AI models, ensuring they can effectively communicate with AI systems to achieve desired results. The test evaluates the candidate's understanding of programming logic, which is crucial for developing solutions in generative AI applications. By focusing on these skills, the assessment helps identify individuals who can contribute to the development and enhancement of AI-driven solutions, making it ideal for roles in the rapidly evolving field of generative AI.

Target Audience

This assessment is suitable for roles such as AI Prompt Engineer, AI Developer, Machine Learning Engineer, and other positions involved in generative AI and AI model development.

Prerequisites

Understanding of AI and machine learning concepts
Familiarity with natural language processing
Basic programming skills
Ability to design and refine prompts for AI models
Problem-solving skills
Knowledge of AI model training and optimization
Experience with generative AI tools and platforms

Test Overview

Duration

60 mins

Questions

Passing Score

70%

Questions

Prompt Design and Problem-Solving

Complex Prompt Design

Prompt Structure Rationale

Technical Article Summarization

What this question evaluates

This question assesses the candidate's ability to design complex prompts and their understanding of the rationale behind prompt structure. It also evaluates their skills in summarizing technical articles.

Type:

Programming

Difficulty:

Medium

Time:

5 mins

Attempts:

100+

Success Rate:

70.01%

Creativity and Innovation

Complex Prompt Design

Prompt Structure Rationale

Technical Article Summarization

What this question evaluates

Type:

Programming

Difficulty:

Hard

Time:

10 mins

Attempts:

100+

Success Rate:

70.01%

Practical Skills Assessment

Complex Prompt Design

Prompt Structure Rationale

Technical Article Summarization

What this question evaluates

Type:

Programming

Difficulty:

Hard

Time:

10 mins

Attempts:

100+

Success Rate:

70.01%

Understanding of Limitations and Ethics

Complex Prompt Design

Prompt Structure Rationale

Technical Article Summarization

What this question evaluates

Type:

Programming

Difficulty:

Hard

Time:

10 mins

Attempts:

100+

Success Rate:

70.01%

Scenario-Based Problem Solving

Complex Prompt Design

Prompt Structure Rationale

Technical Article Summarization

What this question evaluates

Type:

Programming

Difficulty:

Hard

Time:

10 mins

Attempts:

100+

Success Rate:

70.01%

Technical Understanding of AI Models

GPT-4 Architecture

Language Models

Prompt-based Generation

Response Generation

Understanding Language Models

What this question evaluates

This question assesses the candidate's understanding of language models, specifically GPT-4, and their ability to explain how these models generate responses based on prompts. It also evaluates the candidate's knowledge of the underlying architecture of GPT-4 and how it influences response generation.

Type:

Programming

Difficulty:

Medium

Time:

5 mins

Attempts:

100+

Success Rate:

70.01%

Strong Password

Programming

Strings

Verified

What this question evaluates

This question assesses the candidate's ability to write a function in Python that validates a password based on specific requirements. It tests the candidate's understanding of string manipulation, conditional statements, and regular expressions.

Type:

Programming

Difficulty:

Easy

Time:

10 mins

Attempts:

100+

Success Rate:

70.01%

Minimizing Overfitting in Responses

Implementation Skills

Language Model

Overfitting

Prompt Design

Response Broadening

What this question evaluates

This question evaluates the candidate's understanding of strategies to mitigate the issue of a language model being too narrowly focused on examples provided in the prompt. It assesses the ability to identify and implement effective strategies to broaden the responses of a language model.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Avoiding Biased Responses

Bias Minimization

Cultural Sensitivity

Ethical Prompting

Factual Questions

Neutral Prompts

What this question evaluates

This question assesses the candidate's understanding of prompt engineering strategies to minimize bias in language models. It evaluates their knowledge of techniques such as avoiding cultural references, using neutral prompts, asking factual questions, and limiting response length.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Effective Information Retrieval

Effective Questioning

Information Gathering

Information Retrieval

Language Model Usage

Prompt Structuring

What this question evaluates

This question assesses the candidate's ability to structure prompts for obtaining detailed technical information using a language model. It evaluates their understanding of different approaches to structuring prompts and their effectiveness in obtaining the needed information.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Multi-Turn Dialogue Handling

Coherence

Context Maintenance

Context Management

Language Model

Multi-Turn Dialogue

What this question evaluates

This question assesses the candidate's understanding of maintaining context in a multi-turn conversation with a language model. It evaluates the candidate's knowledge of structuring prompts to ensure continuity and coherence in the conversation.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Chain-of-Thought Prompting

Articulation

Chain-of-Thought

Logic Puzzles

Problem Solving

Problem-Solving

What this question evaluates

This question assesses the candidate's understanding of language models and their ability to solve complex logic puzzles. It evaluates their knowledge of different types of prompts and their effectiveness in breaking down problems into solvable parts.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Enhancing Creative Outputs

Creative Writing

Creativity Enhancement

Language Modeling

Poetry Generation

Prompt Design

What this question evaluates

This question assesses the candidate's understanding of prompt strategies for enhancing creativity in generating original poetry using a language model.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Prompt Refinement Techniques

Keyword Addition

Language Model Techniques

Natural Language Understanding

Prompt Modification

Prompt Refinement

What this question evaluates

This question assesses the candidate's knowledge of techniques to improve the specificity of responses in a language model. It tests their understanding of how to make responses more specific by adding specific keywords or questions to the prompt.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Basics of Prompt Engineering

Clarity

Historical Event Description

Knowledge Representation

Language Models

Prompt Design

What this question evaluates

This question assesses the candidate's understanding of prompt engineering for language models. It evaluates the candidate's knowledge of the steps involved in generating a detailed description of a historical event using a language model.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Handling Ambiguous Queries

Clarity

Context Addition

Contextual Understanding

Precision Request

Prompt Clarity

What this question evaluates

This question assesses the candidate's ability to modify a prompt to obtain more specific and clear answers from a language model. It evaluates their understanding of different strategies to improve prompt clarity, such as requesting precision, adding relevant context and details, repeating the question in different ways, and using technical jargon.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

Understanding Contextual Prompts

Contextual Prompting

Creative Story Generation

Creative Use

Language Models

Prompt Types

What this question evaluates

This question assesses the candidate's understanding of language models for creative story generation. It evaluates their knowledge of the different types of prompts and their impact on the quality of generated stories.

Type:

Programming

Difficulty:

Easy

Time:

0.75 mins

Attempts:

100+

Success Rate:

70.01%

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Candidate Experience

Interactive coding environment with real-time feedback

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Built-in code editor with syntax highlighting

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Detailed explanations for correct answers

Time management tools to help pace yourself

Proctoring & Anti-Cheating

Sherlock AI Agent

Sherlock is more than just a tool, it's your AI test integrity agent. By continuously monitoring and analyzing candidate behavior in real-time, Sherlock ensures a secure and fair testing environment. Using machine learning, it detects suspicious patterns, so you can focus on reliable results while Sherlock handles test integrity.

Learn how it works

Live Monitoring

Track behavior with real-time video and audio.

Screen Tracking

Multi-screen detection and continuous screen recording during assessment.

Pattern Analysis

Spot suspicious actions with AI-driven insights.

Access Control

Ensure secure tests with browser lockdown.

Real-time Monitoring

Video Feed

Active

Screen Activity

98%

Focus Rate

95%

Zanele Dlamini

Candidate

Passed

85%

AI Summary

Skills Performance

Score

Prompt Engineering

87%

Programming Logic

80%

85%

82%

Areas of Improvement

Review

Practice

Programming Logic

Skill Assessment

Detailed evaluation of technical skills and problem-solving abilities.

AI Analysis

Machine learning-powered insights into candidate performance patterns.

Benchmarking

Compare results against industry standards and other candidates.

Action Items

Specific recommendations for skill development and improvement.

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