AI Model Designer Test

The AI Model Designer Test evaluates a candidate's expertise in designing AI models, focusing on generative AI, machine learning fundamentals, neural networks, and language modeling. MCQs assess theoretical knowledge, while a coding question tests Python skills for AI model implementation and problem-solving. This test is ideal for roles such as AI Model Designer, Data Scientist, and Machine Learning Engineer.

Covered skills:

Generative AI Concepts
Machine Learning Principles
Neural Network Architectures
Language Modeling Techniques
Python Programming
AI Model Evaluation
Data Preprocessing for AI
Deep Learning Fundamentals
AI Ethics and Fairness
Optimization in AI
Natural Language Processing Basics
AI Model Deployment

Test Duration

~ 40 mins

Difficulty Level

Moderate

Questions

4 Generative AI MCQs
4 Machine Learning MCQs
4 Neural Networks MCQs
4 Language Modeling (LLMs) MCQs
1 Python Coding Question

Availability

Available as custom test

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About the AI Model Designer Assessment Test

The AI Model Designer 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:

Understand the fundamental concepts of generative AI models
Develop and implement basic machine learning algorithms
Design neural networks for diverse applications
Evaluate and select appropriate language modeling techniques
Program in Python for AI and machine learning tasks
Critically analyze AI model performance and limitations
Apply effective data preprocessing methods for AI readiness
Understand the ethical implications and fairness issues in AI
Deploy AI models in real-world scenarios efficiently
Optimize neural network architectures for improved accuracy
Grasp the essentials of deep learning and its applications
Implement basic natural language processing techniques

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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 AI Model Designer Test will be non-googleable.

🧐 Question
Easy Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	Solve
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
Medium Less complex decision tree model Model Complexity Overfitting Data Transformation Overfitting Prevention	Solve
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
Easy n-gram generator String Manipulation Algorithm	Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Solve
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
Easy Sensitivity and Specificity Confusion Matrix Model Evaluation Sensitivity Specificity	Solve
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill
	Easy Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	2 mins Machine Learning	Solve
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
	Medium Less complex decision tree model Model Complexity Overfitting Data Transformation Overfitting Prevention	2 mins Machine Learning	Solve
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
	Easy n-gram generator String Manipulation Algorithm	2 mins Machine Learning	Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	2 mins Machine Learning	Solve
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
	Easy Sensitivity and Specificity Confusion Matrix Model Evaluation Sensitivity Specificity	2 mins Machine Learning	Solve
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	Machine Learning	Easy	2 mins	Solve
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
	Less complex decision tree model Model Complexity Overfitting Data Transformation Overfitting Prevention	Machine Learning	Medium	2 mins	Solve
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
	n-gram generator String Manipulation Algorithm	Machine Learning	Easy	2 mins	Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Machine Learning	Easy	2 mins	Solve
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
	Sensitivity and Specificity Confusion Matrix Model Evaluation Sensitivity Specificity	Machine Learning	Easy	2 mins	Solve
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

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Why you should use Pre-employment AI Model Designer Test?

The AI Model Designer 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:

Understanding generative AI concepts and models.
Applying machine learning principles effectively.
Implementing basic neural network architectures.
Developing language modeling techniques.
Programming with Python for AI tasks.
Evaluating AI models using standard metrics.
Preprocessing data for AI applications.
Utilizing deep learning fundamentals in projects.
Understanding AI ethics and fairness.
Optimizing AI models for performance.

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 AI Model Designer Test?

Generative AI Concepts: Generative AI involves using algorithms to create data that mimics real-world examples, such as text, images, or music. Understanding these concepts is crucial for developing models capable of producing novel and creative solutions to complex problems.

Machine Learning Principles: This domain covers foundational ideas such as supervised and unsupervised learning, which form the basis for building predictive models. A solid grasp of machine learning principles enables the designing of robust systems capable of learning from and adapting to data.

Neural Network Architectures: Neural network architectures refer to the design of layers and connections that enable deep learning models to process data. This knowledge is essential for creating networks that are optimized for specific tasks like image recognition or language processing.

Language Modeling Techniques: These techniques aim to predict the probability of a sequence of words, which is crucial in natural language processing tasks such as translation and text generation. Understanding language modeling helps in creating models that can effectively understand and generate human language.

Python Programming: Python is a versatile programming language commonly used in AI development for its extensive libraries and community support. Proficiency in Python is critical for implementing and deploying machine learning models efficiently.

AI Model Evaluation: Evaluating AI models involves assessing their performance based on metrics such as accuracy, precision, and recall, ensuring that they meet desired standards. This skill is vital for determining the reliability and viability of AI solutions.

Data Preprocessing for AI: Data preprocessing involves cleaning and transforming raw data into a format suitable for analysis, significantly impacting the effectiveness of AI models. Proper preprocessing ensures that models are trained on accurate and relevant data.

Deep Learning Fundamentals: Deep learning focuses on neural networks with many layers, enabling advanced data representations and feature detection. These fundamentals are key to innovating in areas such as computer vision and automated speech recognition.

AI Ethics and Fairness: Ethics and fairness in AI address the societal impact of AI technologies, ensuring that models operate without bias and promote equity. This consideration is central to the responsible development and deployment of AI systems.

Optimization in AI: Optimization is the process of making models more efficient and effective by refining their behavior and reducing error rates. It is crucial for enhancing the performance and speed of AI applications.

Natural Language Processing Basics: Natural language processing (NLP) involves the interaction between computers and human language, facilitating tasks like sentiment analysis and chatbot design. Mastery of NLP basics is essential for building applications that process and understand textual data.

AI Model Deployment: Deploying AI models involves transitioning them from development to real-world environments where they can provide value. Understanding deployment techniques ensures models are effectively integrated and scaled in production settings.

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 AI Model Designer Test to be based on.

GANs

VAEs

Transfer Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

CNNs

RNNs

LSTMs

Transformers

Text Embeddings

Word2Vec

BERT

GPT

Encoder-Decoder

Gradient Descent

Backpropagation

Overfitting

Bias and Variance

Python Syntax

Numpy Arrays

Pandas Dataframes

Matplotlib Plots

Scikit-learn

Data Cleaning

Feature Engineering

Model Validation

Cross-Validation

ROC Curve

Confusion Matrix

Precision-Recall

Regularization

Dropout

Batch Normalization

Ethical AI

AI Fairness

ML Deployment

REST APIs

Docker

PyTorch

TensorFlow

Keras

NLP Basics

Tokenization

Sentiment Analysis

Stemming

Lemmatization

What roles can I use the AI Model Designer Test for?

AI Model Designer
Machine Learning Engineer
Data Scientist
AI Research Scientist
Software Developer
Deep Learning Specialist
AI Engineer
Data Analyst
NLP Scientist
AI Product Manager

How is the AI Model Designer 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

Designing complex neural network architectures.
Advanced language modeling for NLP tasks.
Integrating AI models into deployment pipelines.
Applying sophisticated data preprocessing techniques.
Implementing advanced deep learning methodologies.
Conducting rigorous AI model evaluations.
Ensuring fairness and ethical use of AI.
Utilizing optimization techniques in AI.
Employing advanced natural language processing.
Scaling AI solutions in production environments.

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

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Have questions about the AI Model Designer Hiring Test?

What is AI Model Designer Test?

The AI Model Designer Test is designed to evaluate the skills required for designing AI models, specifically in areas like generative AI, machine learning, and more. Recruiters use this test to assess candidates for roles involving AI model design.

Can I combine the AI Model Designer Test with language modeling questions?

Yes, recruiters can create a custom test including various skills such as language modeling. For more details, you can check out our Natural Language Processing (NLP) Online Test.

What topics are evaluated in the AI Model Designer Test?

This test evaluates topics such as Generative AI Concepts, Machine Learning Principles, Neural Network Architectures, Language Modeling Techniques, and Python Programming. It's tailored to assess the skills necessary for senior roles in AI model design.

How to use AI Model Designer Test in my hiring process?

Incorporate this test early in your recruitment process as a screening tool by sharing the test link in job posts or direct email invites, ensuring you gauge critical skills from the outset.

Can I test Python and AI Model Designing together in a test?

Yes, you can test Python and AI Model Designing together for comprehensive evaluation. For more information, check our Python & AI 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:

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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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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