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Agentic AI Engineer Test

The Agentic AI Engineer Test assesses a candidate's expertise in developing AI-driven systems, focusing on topics like generative AI, agent development, and prompt engineering. It evaluates proficiency in Python through coding questions and tests understanding of AI algorithms, machine learning, and natural language processing via MCQs.

Covered skills:

  • Prompt Engineering
  • Generative AI
  • Agent Development
  • Python Programming
  • AI Algorithms
  • Machine Learning Concepts
  • Natural Language Processing
  • Responsible AI
  • Software Development Processes
  • Debugging and Testing Practices
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About the Agentic AI Engineer Assessment Test


The Agentic AI Engineer Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Crafting precise and effective prompts for AI models
  • Leveraging generative AI to create innovative solutions
  • Designing and developing intelligent agents
  • Writing clean and efficient Python code
  • Understanding and implementing AI algorithms
  • Grasping core machine learning concepts
  • Applying natural language processing techniques
  • Adhering to responsible AI principles
  • Following software development best practices
  • Effectively debugging and testing code

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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 Agentic AI Engineer 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)

Medium

ZeroDivisionError and IndexError
Exceptions
Exception Handling
Error Handling
Solve
What will the following Python code output?
 image

Medium

Session
File Handling
Dictionary
Csv Parsing
Exception Handling In File Input/output
Solve
 image
The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file:
Tony,10
Stark,12
Black,25
Your program should ignore a non-conforming line like this one.
Stark,3
Widow,6
Widow,14
The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 }
What should replace the CODE TO FILL line to complete the function?
 image

Medium

Max Code
Arrays
Code Analysis
Algorithm Understanding
Solve
Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:
 image

Medium

Recursive Function
Recursion
Dictionary
Lists
Solve
Consider the following Python code:
 image
In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None.

nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target.

What will the output be after executing the above code?

Medium

Stacking problem
Stack
Linkedlist
Solve
What does the below function ‘fun’ does?
 image
A: Sum of digits of the number passed to fun.
B: Number of digits of the number passed to fun.
C: 0 if the number passed to fun is divisible by 10. 1 otherwise.
D: Sum of all digits number passed to fun except for the last digit.
🧐 Question 🔧 Skill

Easy

JSON Prompt Design
JSON structure
Prompt crafting
Data types

2 mins

Prompt Engineering
Solve

Medium

Temperature Settings
Temperature calibration
Generative behavior
Output variability analysis

2 mins

Prompt Engineering
Solve

Medium

ZeroDivisionError and IndexError
Exceptions
Exception Handling
Error Handling

2 mins

Python
Solve

Medium

Session
File Handling
Dictionary
Csv Parsing
Exception Handling In File Input/output

2 mins

Python
Solve

Medium

Max Code
Arrays
Code Analysis
Algorithm Understanding

2 mins

Python
Solve

Medium

Recursive Function
Recursion
Dictionary
Lists

3 mins

Python
Solve

Medium

Stacking problem
Stack
Linkedlist

4 mins

Python
Solve
🧐 Question 🔧 Skill 💪 Difficulty ⌛ Time
JSON Prompt Design
JSON structure
Prompt crafting
Data types
Prompt Engineering
Easy 2 mins
Solve
Temperature Settings
Temperature calibration
Generative behavior
Output variability analysis
Prompt Engineering
Medium 2 mins
Solve
ZeroDivisionError and IndexError
Exceptions
Exception Handling
Error Handling
Python
Medium 2 mins
Solve
Session
File Handling
Dictionary
Csv Parsing
Exception Handling In File Input/output
Python
Medium 2 mins
Solve
Max Code
Arrays
Code Analysis
Algorithm Understanding
Python
Medium 2 mins
Solve
Recursive Function
Recursion
Dictionary
Lists
Python
Medium 3 mins
Solve
Stacking problem
Stack
Linkedlist
Python
Medium 4 mins
Solve
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Why you should use Pre-employment Agentic AI Engineer Test?

The Agentic AI Engineer Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Creating effective AI prompts.
  • Utilizing generative AI models.
  • Developing autonomous AI agents.
  • Implementing Python code solutions.
  • Applying basic AI algorithms.
  • Understanding machine learning concepts.
  • Processing language using NLP techniques.
  • Ensuring responsible AI practices.
  • Conducting software development processes.
  • Debugging and testing Python code.

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Agentic AI Engineer Test?

Prompt Engineering: Prompt Engineering involves designing and refining input queries or instructions to effectively interact with AI models. This skill ensures that the AI systems interpret and generate responses accurately, which is crucial for any application involving natural language understanding or generation.

Generative AI: Generative AI encompasses techniques where models create new content, such as text or images, based on training data. Measuring this skill highlights the ability to leverage AI for innovation, pushing the boundaries of creativity with machine-generated results.

Agent Development: Agent Development focuses on creating autonomous entities capable of perceiving their environment and taking actions to achieve goals. This skill is critical for building applications that require independent decision-making or interaction without continuous human input.

Python Programming: Python Programming is essential due to its widespread use in data science, machine learning, and AI development. A strong grasp of Python facilitates efficient script creation and integration with various AI libraries and frameworks.

AI Algorithms: Understanding AI Algorithms is foundational in developing models that can learn and make predictions. It's vital to assess this skill to ensure candidates can implement and optimize these algorithms effectively.

Machine Learning Concepts: Machine Learning Concepts involve grasping the theories and principles that underpin data-driven model development and learning. This knowledge is core to applying AI in practical situations and solving complex problems.

Natural Language Processing: Natural Language Processing (NLP) enables machines to understand and generate human language. This expertise is crucial in developing applications such as chatbots, translators, and other language-based AI tools.

Responsible AI: Responsible AI encompasses the ethical and fair implementation of AI technologies. Measuring this skill ensures that AI practitioners consider fairness, accountability, transparency, and ethical implications in their work.

Software Development Processes: Software Development Processes provide a structured approach to planning, creating, testing, and deploying software applications. This is crucial for ensuring robust and maintainable AI solutions within software systems.

Debugging and Testing Practices: Debugging and Testing Practices are critical for identifying and resolving defects or issues in AI systems, ensuring their reliability and performance. Mastery of these practices contributes to the development of high-quality, error-free applications.

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 Agentic AI Engineer Test to be based on.

Prompt Design
Prompt Optimization
Generative Models
AI Creativity
Agent Architecture
Agent Coordination
Python Syntax
Python Libraries
AI Algorithms
Supervised Learning
Unsupervised Learning
Deep Learning
NLP Techniques
Language Models
Model Fine-tuning
Ethical AI
Bias Mitigation
Code Review
Version Control
Automated Testing
Test Cases
Error Handling
Data Structures
Algorithm Design
Time Complexity
Space Complexity
Python Testing
ML Frameworks
Neural Networks
Reinforcement Learning
Generative Adversarial Networks
Agent Communication
API Development
Web Scraping
Data Analysis
Data Visualization
Functional Programming
Object-Oriented Programming
Debugging Tools
Performance Optimization
Concurrency
Parallel Processing
Natural Language Understanding
Tokenization
Embedding Techniques
Responsible AI Guidelines
Software Lifecycle
Continuous Integration
Continuous Deployment
Git Branching
Git Merging

What roles can I use the Agentic AI Engineer Test for?

  • AI Engineer
  • Machine Learning Engineer
  • Data Scientist
  • Software Developer
  • Research Scientist
  • Python Developer
  • AI Researcher
  • Automation Engineer
  • AI Consultant
  • Technical Architect

How is the Agentic AI Engineer Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • Optimizing AI model performance.
  • Managing AI agent lifecycle.
  • Improving prompt engineering techniques.
  • Leveraging advanced generative AI features.
  • Employing underlying Python libraries.
  • Designing complex AI algorithms.
  • Applying machine learning models.
  • Streamlining NLP pipeline processes.
  • Applying ethical AI principles.
  • Enhancing software design and architecture.

The coding question for experienced candidates will be of a higher difficulty level to evaluate more hands-on experience.

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AI Cheating Detection with Honestly

ChatGPT Protection

Non-googleable Questions

Web Proctoring

IP Proctoring

Webcam Proctoring

MCQ Questions

Coding Questions

Typing Questions

Personality Questions

Custom Questions

Ready-to-use Tests

Custom Tests

Custom Branding

Bulk Invites

Public Links

ATS Integrations

Multiple Question Sets

Custom API integrations

Role-based Access

Priority Support

GDPR Compliance

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Have questions about the Agentic AI Engineer Hiring Test?

What is Agentic AI Engineer Test?

The Agentic AI Engineer Test is designed to evaluate a candidate's expertise in AI-related domains such as Prompt Engineering, Generative AI, Agent Development, and Python Programming. It's an excellent tool for recruiters to assess capabilities in AI and machine learning roles.

Can I combine the Agentic AI Engineer Test with Machine Learning questions?

Yes, you can request a custom test that includes Machine Learning questions. For details on how we assess Machine Learning, please check our Machine Learning Assessment Test.

What kind of questions are used to evaluate senior candidates in the Agentic AI Engineer Test?

For senior roles, the test focuses on optimizing AI model performance, managing AI agent lifecycle, improving prompt engineering techniques, and leveraging advanced features of generative AI and Python libraries.

How to use the Agentic AI Engineer Test in my hiring process?

Integrate this test into your recruitment as a pre-screening step. You can add a link to the assessment in job postings or invite candidates via email, ensuring you shortlist highly skilled candidates quickly.

What are the main AI and Machine Learning tests?

In addition to the Agentic AI Engineer Test, key assessments include:

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:

  • Hidden AI Tools Detection with Honestly
  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

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.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

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

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