Prompt Engineering Online Test

The Prompt Engineering Online Test evaluates a candidate's ability to design and optimize prompts for AI models effectively. Utilizing MCQ questions, it assesses the understanding of AI outputs, iterative prompting, evaluation techniques, and ethical practices. It aims to identify skills in writing and refining prompts for diverse applications.

Covered skills:

Prompt Writing
Prompt Optimization
Understanding AI Output
Iterative Prompting
Prompt Evaluation Techniques
AI Model Limitations
Use of Context in Prompts
Prompt Templates
Bias Mitigation in Prompts
Prompting for Data Analysis
Ethical Prompting Practices
Prompt Testing and Validation

Test Duration

~ 25 mins

Difficulty Level

Moderate

Questions

10 Prompt Engineering MCQs

Availability

Available as custom test

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About the Prompt Engineering Test

The Prompt Engineering Online Test helps recruiters and hiring managers assess candidates' skills in crafting and optimizing prompts for AI models. It aids in identifying individuals who can effectively communicate with AI, leading to better model outputs and more impactful AI applications. This test streamlines the hiring process by filtering candidates based on their practical prompt engineering abilities.

The test evaluates a candidate's understanding of prompt writing principles, optimization techniques, and AI output analysis. It also examines their ability to iterate on prompts, evaluate their performance, and understand the limitations of AI models. Furthermore, the test probes their knowledge of using context in prompts, applying prompt templates, mitigating bias, prompting for data analysis, adhering to ethical prompting practices, and validating prompt performance.

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Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

These are just a small sample from our library of 15,000+ questions. The actual questions on this Prompt Engineering Online Test will be non-googleable.

🧐 Question
Easy JSON Prompt Design JSON structure Prompt crafting Data types	Solve
You are asked to create a prompt for a language model that outputs JSON data for a company's employee database. The JSON must include an employee's ID, name, age, and whether they are currently active. Consider how you might structure your prompt given these fields. Identify the best prompt design. Example JSON output: { "ID": "123", "Name": "John Doe", "Age": 30, "Active": true } Which prompt structure would most effectively guide the language model to generate the correct JSON format? A: Create JSON objects for employees with fields: ID, Name, Age, Active. ID should be a string. B: Generate JSON data: ID, Name, Age, Active. ID is a number. C: Output JSON: ID (string), Name, Age (integer), Active (boolean). D: Make JSON: ID, Name, Age, Active. ID is always number. E: Design JSON response: ID, Name, Age, Active with clear typing. F: Provide JSON example: Employee data with structured fields.
Medium Temperature Settings Temperature calibration Generative behavior Output variability analysis	Solve
Consider the following prompt given to an LLM: System Prompt: "You are a creative writing assistant helping authors develop character descriptions. Use vivid imagery and engaging language to bring characters to life." Main Prompt: "Write a character description for a mysterious librarian with the following traits: Works in an ancient library Has silver hair and green eyes Knows secrets about forbidden books Appears to be in their 40s but seems much older Always wears a pendant with strange symbols" Three outputs were generated with different temperature settings: Output A: "The librarian possesses silver hair and green eyes. Age appears to be approximately 40 years. Works in an ancient library facility. Has knowledge of restricted book collections. Wears jewelry featuring symbolic markings. Maintains professional demeanor while handling library materials." Output B: "Elara moves through the ancient library like a ghost haunting familiar halls, her silver hair catching moonbeams that slip through Gothic windows. Those emerald eyes hold centuries of secrets—too many secrets for someone who claims only four decades of life. The obsidian pendant at her throat pulses with runes that predate any known language, and when she speaks of the forbidden texts, her voice carries the weight of ages. There's something unsettling about how she never seems surprised by anything, as if she's been waiting lifetimes for each visitor to find their way to her domain." Output C: "Meet Elara, the enigmatic keeper of knowledge whose silver locks frame a face that defies time itself. Her piercing green eyes seem to peer directly into your soul, holding secrets that could unravel reality. The ancient library around her creaks with whispered stories, and she alone holds the keys to the forbidden sections where dangerous truths slumber. The mystical pendant adorning her neck bears symbols older than civilization, pulsing with otherworldly energy that hints at her true nature." Which temperature setting was most likely used for each output? A) Output A (T=0.1), Output B (T=1.0), Output C (T=0.5) B) Output A (T=0.5), Output B (T=0.1), Output C (T=1.0) C) Output A (T=0.1), Output B (T=0.5), Output C (T=1.0) D) Output A (T=1.0), Output B (T=0.5), Output C (T=0.1) E) Output A (T=0.5), Output B (T=1.0), Output C (T=0.1)

	🧐 Question	🔧 Skill
	Easy JSON Prompt Design JSON structure Prompt crafting Data types	2 mins Prompt Engineering	Solve
You are asked to create a prompt for a language model that outputs JSON data for a company's employee database. The JSON must include an employee's ID, name, age, and whether they are currently active. Consider how you might structure your prompt given these fields. Identify the best prompt design. Example JSON output: { "ID": "123", "Name": "John Doe", "Age": 30, "Active": true } Which prompt structure would most effectively guide the language model to generate the correct JSON format? A: Create JSON objects for employees with fields: ID, Name, Age, Active. ID should be a string. B: Generate JSON data: ID, Name, Age, Active. ID is a number. C: Output JSON: ID (string), Name, Age (integer), Active (boolean). D: Make JSON: ID, Name, Age, Active. ID is always number. E: Design JSON response: ID, Name, Age, Active with clear typing. F: Provide JSON example: Employee data with structured fields.
	Medium Temperature Settings Temperature calibration Generative behavior Output variability analysis	2 mins Prompt Engineering	Solve
Consider the following prompt given to an LLM: System Prompt: "You are a creative writing assistant helping authors develop character descriptions. Use vivid imagery and engaging language to bring characters to life." Main Prompt: "Write a character description for a mysterious librarian with the following traits: Works in an ancient library Has silver hair and green eyes Knows secrets about forbidden books Appears to be in their 40s but seems much older Always wears a pendant with strange symbols" Three outputs were generated with different temperature settings: Output A: "The librarian possesses silver hair and green eyes. Age appears to be approximately 40 years. Works in an ancient library facility. Has knowledge of restricted book collections. Wears jewelry featuring symbolic markings. Maintains professional demeanor while handling library materials." Output B: "Elara moves through the ancient library like a ghost haunting familiar halls, her silver hair catching moonbeams that slip through Gothic windows. Those emerald eyes hold centuries of secrets—too many secrets for someone who claims only four decades of life. The obsidian pendant at her throat pulses with runes that predate any known language, and when she speaks of the forbidden texts, her voice carries the weight of ages. There's something unsettling about how she never seems surprised by anything, as if she's been waiting lifetimes for each visitor to find their way to her domain." Output C: "Meet Elara, the enigmatic keeper of knowledge whose silver locks frame a face that defies time itself. Her piercing green eyes seem to peer directly into your soul, holding secrets that could unravel reality. The ancient library around her creaks with whispered stories, and she alone holds the keys to the forbidden sections where dangerous truths slumber. The mystical pendant adorning her neck bears symbols older than civilization, pulsing with otherworldly energy that hints at her true nature." Which temperature setting was most likely used for each output? A) Output A (T=0.1), Output B (T=1.0), Output C (T=0.5) B) Output A (T=0.5), Output B (T=0.1), Output C (T=1.0) C) Output A (T=0.1), Output B (T=0.5), Output C (T=1.0) D) Output A (T=1.0), Output B (T=0.5), Output C (T=0.1) E) Output A (T=0.5), Output B (T=1.0), Output C (T=0.1)

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	JSON Prompt Design JSON structure Prompt crafting Data types	Prompt Engineering	Easy	2 mins	Solve
You are asked to create a prompt for a language model that outputs JSON data for a company's employee database. The JSON must include an employee's ID, name, age, and whether they are currently active. Consider how you might structure your prompt given these fields. Identify the best prompt design. Example JSON output: { "ID": "123", "Name": "John Doe", "Age": 30, "Active": true } Which prompt structure would most effectively guide the language model to generate the correct JSON format? A: Create JSON objects for employees with fields: ID, Name, Age, Active. ID should be a string. B: Generate JSON data: ID, Name, Age, Active. ID is a number. C: Output JSON: ID (string), Name, Age (integer), Active (boolean). D: Make JSON: ID, Name, Age, Active. ID is always number. E: Design JSON response: ID, Name, Age, Active with clear typing. F: Provide JSON example: Employee data with structured fields.
	Temperature Settings Temperature calibration Generative behavior Output variability analysis	Prompt Engineering	Medium	2 mins	Solve
Consider the following prompt given to an LLM: System Prompt: "You are a creative writing assistant helping authors develop character descriptions. Use vivid imagery and engaging language to bring characters to life." Main Prompt: "Write a character description for a mysterious librarian with the following traits: Works in an ancient library Has silver hair and green eyes Knows secrets about forbidden books Appears to be in their 40s but seems much older Always wears a pendant with strange symbols" Three outputs were generated with different temperature settings: Output A: "The librarian possesses silver hair and green eyes. Age appears to be approximately 40 years. Works in an ancient library facility. Has knowledge of restricted book collections. Wears jewelry featuring symbolic markings. Maintains professional demeanor while handling library materials." Output B: "Elara moves through the ancient library like a ghost haunting familiar halls, her silver hair catching moonbeams that slip through Gothic windows. Those emerald eyes hold centuries of secrets—too many secrets for someone who claims only four decades of life. The obsidian pendant at her throat pulses with runes that predate any known language, and when she speaks of the forbidden texts, her voice carries the weight of ages. There's something unsettling about how she never seems surprised by anything, as if she's been waiting lifetimes for each visitor to find their way to her domain." Output C: "Meet Elara, the enigmatic keeper of knowledge whose silver locks frame a face that defies time itself. Her piercing green eyes seem to peer directly into your soul, holding secrets that could unravel reality. The ancient library around her creaks with whispered stories, and she alone holds the keys to the forbidden sections where dangerous truths slumber. The mystical pendant adorning her neck bears symbols older than civilization, pulsing with otherworldly energy that hints at her true nature." Which temperature setting was most likely used for each output? A) Output A (T=0.1), Output B (T=1.0), Output C (T=0.5) B) Output A (T=0.5), Output B (T=0.1), Output C (T=1.0) C) Output A (T=0.1), Output B (T=0.5), Output C (T=1.0) D) Output A (T=1.0), Output B (T=0.5), Output C (T=0.1) E) Output A (T=0.5), Output B (T=1.0), Output C (T=0.1)

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Why you should use Pre-employment Prompt Engineering Online Test?

The Prompt Engineering Online Test is designed to quickly assess a candidate's aptitude in the burgeoning field of prompt engineering. It provides a standardized way to gauge their practical skills and identify individuals who can effectively interact with and guide AI models. This assessment is a valuable tool for streamlining the hiring process and ensuring that new team members possess the capabilities needed to leverage AI effectively.

The questions in this test evaluate a candidate's grasp of key prompt engineering concepts. They gauge a candidate's skill in prompt writing and optimization techniques, and their ability to understand and interpret AI model outputs. The assessment also examines knowledge of iterative prompting strategies, prompt evaluation methods, and awareness of AI model limitations. We also test for the understanding of how context impacts prompts, use of templates, and finally basic understanding of bias mitigation.

Upon completion of the test, you will receive a detailed scorecard for each candidate, providing a breakdown of their skills and benchmarks for comparison. This report allows you to identify top candidates and make informed decisions about hiring and team placement.

What topics are covered in the Prompt Engineering Online Test?

This test gauges a candidate's skill in Prompt Writing, the ability to craft clear and effective instructions for AI models. It also measures their understanding of AI Output, ensuring they can interpret and utilize the information provided by AI systems.

Furthermore, the assessment evaluates Prompt Optimization, reflecting the candidate's capability to refine prompts for better results. Testing this skill ensures candidates can fine-tune their requests to elicit the most pertinent and valuable responses from AI. The test also covers Iterative Prompting, which is the ability to refine prompts based on previous outputs and is a key ingredient for better AI outputs.

The test also incorporates Prompt Evaluation Techniques, assessing the ability to judge the quality and relevance of AI-generated content. Candidates are tested on AI Model Limitations, demonstrating knowledge of what AI can and cannot do. Recognizing these boundaries enables realistic expectations and prevents misuse of AI tools.

The assessment also evaluates Use of Context in Prompts, reflecting how well candidates can integrate relevant information to guide AI responses. The inclusion of Prompt Templates highlights the ability to create structured and reusable prompts. This streamlines the prompting process and promotes uniformity in AI interactions.

Additionally, the evaluation addresses Bias Mitigation in Prompts, showing understanding of how to create prompts that minimize prejudiced or discriminatory outcomes. Candidates are assessed on Prompting for Data Analysis, indicating their capacity to leverage AI for extracting meaningful insights from data. This skill is useful for understanding trends, patterns, and correlations.

Finally, the test explores Ethical Prompting Practices, highlighting the awareness of moral considerations when interacting with AI. The test also measures Prompt Testing and Validation, which is the ability to rigorously test and validate prompts to ensure reliability. It is important that the candidate can identify any discrepancies or areas for improvement.

Full list of covered topics

The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Prompt Engineering Online Test to be based on.

Prompt Syntax

Command Clarity

Response Analysis

Feedback Iteration

Contextual Prompts

Prompt Efficiency

Prompt Clarity

Bias Awareness

Ethical Considerations

Prompt Validation

Test Adaptability

AI Output Interpretation

Model Limitations

Prompt Refinement

Context Application

Prompt Templates

Data Insights

Bias Inspection

Result Evaluation

Prompt Optimization

Scenario Testing

Data Context

Use Case Identification

Machine Understanding

Natural Language Processing

Language Bias

Cultural Sensitivity

Template Usage

Behavior Tuning

Prompts REPETITIVE

Prompt Murkiness

Iterative Testing

Ethical Implications

Linguistic Nuance

Model Constraints

Feedback Loops

AI Debugging

Data Prompting

Limit Awareness

Controlled Outputs

Prompt Adjustments

Analytical Validation

Prompt Patterns

Clarification

Mitigation

Interpretation

Adaptive Prompts

Rigorous Testing

Output Precision

Prompt Nuances

Verification

What roles can I use the Prompt Engineering Online Test for?

AI Specialist
Data Scientist
Machine Learning Engineer
AI Product Manager
AI Research Scientist
UX Designer
Technical Content Writer
AI Consultant
Software Developer
Data Analyst

How is the Prompt Engineering Online Test customized for senior candidates?

For seasoned candidates, the Prompt Engineering Online Test shifts its focus to more intricate and nuanced aspects of prompt engineering. The evaluation probes into their capabilities in areas that demand deeper understanding of model behavior and advanced prompting methodologies.

Specifically, the questions for experienced candidates target their proficiency in areas such as applying prompts for data analysis tasks and demonstrating knowledge of ethical considerations in prompting. We assess their ability to test and validate prompts, while also evaluating their grasp of AI model limitations in complex scenarios. The test is also designed to see how they would understand use of context in prompts, along with prompt engineering templates.

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Have questions about the Prompt Engineering Hiring Test?

What is Prompt Engineering Online Test?

The Prompt Engineering Online Test assesses skills related to writing, optimizing, and evaluating prompts for AI models. It is used by recruiters to identify candidates capable of crafting effective and ethical prompts for AI systems.

Can I combine Prompt Engineering Online Test with Python skills?

Yes, recruiters can request a custom test that combines prompt engineering with Python skills. For more details on assessing Python skills, check our Python Online Test.

What skills are evaluated in the Prompt Engineering Online Test?

This test evaluates prompt writing, optimization, iterative prompting, understanding AI output, AI model limitations, and ethical prompting practices. It also covers advanced skills like designing prompts for complex reasoning and multi-modal AI interactions.

How to use Prompt Engineering Online Test in my hiring process?

Use it as a pre-screening tool by embedding it in job posts or directly emailing candidates. This approach helps to identify skilled candidates early in the hiring process.

Can I test AI Model Integration and Prompt Engineering together?

Unfortunately, there is no direct test combining AI Model Integration and Prompt Engineering in our database. However, you can create a custom test combining the skills you need.

What are the main AI-related tests?

Our main AI-related tests include Deep Learning Test, Machine Learning Test, and NLP Online Test.

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

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Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

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Here is a quick guide on how to request a custom assessment on Adaface.

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Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

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