If you are at the beginning of your professional career as an AI engineer and are wondering how you can be properly prepared for your next job interview, then today's article is for you.

In today's guide, we have collected 9 basic and important questions that you may be asked about your role as an AI engineer, to be properly informed and able to answer methodically and effectively.

Let's start with the first question on our list.

 Question #1: What is the Difference Between Artificial Intelligence, Machine Learning and Deep Learning?

Artificial intelligence, machine learning, and deep learning are related but distinct disciplines.

So if they ask you this question, you can provide the following answer.

   Answer:

Artificial intelligence (AI) is a broad field that includes techniques for creating intelligent systems that can perform tasks that normally require human intelligence.

Machine learning is a branch of artificial intelligence that involves the development of algorithms and models that allow systems to improve their performance over time by learning from data.

Deep Learning is a subset of machine learning that involves the use of neural networks to learn from data.

Deep learning techniques are used for tasks such as image recognition and speech recognition.

 Question #2: What Is the Ideal Dataset Size When Testing an Algorithm?

This question can help the interviewer determine what you know about how to test algorithms and how to use a dataset efficiently.

An indicative answer is the following:

   Answer:

The ideal size of a data set depends on the type of algorithm we are testing.

For example, if we are testing an artificial neural network algorithm, we will need a large enough data set so that the network can learn from it and make accurate predictions.

Of course, if we are testing a machine learning algorithm that uses regression analysis, a smaller data set will be needed to achieve more accurate results.

 Question #3: What Machine Learning Frameworks Have You Worked With?

As an AI engineer, there are a plethora of Machine Learning frameworks that you have worked with.

Also, each has its own capabilities and use cases.

Some such popular frameworks that you may have worked with and you can mention are the following:

   Answer:

Some of the Machine Learning frameworks I have worked with are:

- Scikit-learn

- TensorFlow

- Keras

- Apache Spark MLlib

- H2O

- Shogun

- PyTorch

- Amazon Machine Learning

You can also mention that the choice of each framework depends on the requirements of each project, such as the size and type of data.

 Question #4: Explain the Architecture of a Convolutional Neural Network (CNN) and its Typical Applications

A convolutional neural network (CNN) is a type of artificial neural network designed to process and analyze structured grid data, such as images.

An indicative answer, therefore, is the following:

   Answer:

The architecture of a convolutional neural network (CNN) consists of the following layers:

Input Layer: This layer accepts the raw input data, such as an image represented as a grid of pixel values.

Convolutional Layer: This layer applies a set of filters to the input data to extract features.

ReLU (Rectified Linear Unit) Layer: This layer applies the ReLU activation function to the output of the convolutional layer.

Pooling Layer: This layer reduces the spatial dimensions of the input volume, reducing the possibility of overfitting.

Fully Connected Layer: This layer takes the output of the previous layers and converts it into a single vector.

Output Layer: This layer produces the final classification results.

Some of the typical applications of CNNs include:

- Object detection

- Image segmentation

- Autonomous systems

 Question #5: Describe a Project In Which You Applied NLP Techniques

   Answer:

A project where I applied natural language processing (NLP) techniques involved the development of a sentiment analysis tool for social media data.

The way I approached the project was as follows:

First, I collected a large dataset of social media posts related to the product.

This dataset included text data from various platforms such as Facebook and Instagram.

Then, the text data was pre-processed to remove noise and irrelevant information.

I used techniques like Bag of Words and TF-IDF to convert the text into numerical features that could be fed into a machine-learning model.

To select the appropriate model, I experimented with different machine learning models for sentiment classification, including Naive Bayes and Support Vector Machines (SVM).

The models were trained and evaluated on a separate test set.

Next, for hyper-parameterization, I used grid search and cross-validation to find the optimal hyperparameters for the selected model.

Finally, once the model performed well, it was developed to analyze social media posts in real-time and provide insights regarding audience sentiment towards the product.

 Question #6: How Do You Evaluate the Performance of a Machine Learning Model?

Evaluating the performance of a machine learning model is an important step for an AI engineer to understand how well the model can generalize to new data.

   Answer:

There are various metrics and techniques for evaluating the performance of a machine learning model, depending on the type of problem it needs to solve.

Some common metrics for classification and regression tasks are:

Accuracy: This is the simplest metric and is defined as the number of correct predictions divided by the total number of predictions.

Recall (Sensitivity): Recall is the ratio of true positives to the sum of true positives and false negatives and measures the percentage of true positives that are correctly recognized.

F1 Score: The F1 score is the harmonic mean of accuracy and recall, and is particularly useful when dealing with unbalanced data sets.

Cross-Validation: k-fold cross-validation is used to evaluate the performance of the model on different subsets of data.

 Question #7: What Are Some Common Techniques for Optimizing Deep Learning Models?

To optimize deep learning models, AI engineers can apply various techniques.

So an indicative answer is the following.

   Answer:

Some techniques for optimizing deep learning models are the following:

Gradient Descent: A basic optimization algorithm, which iteratively adjusts the model parameters to minimize the loss function.

Hyper-parameter optimization: This involves tuning the model's hyper-parameters, such as learning rate and number of layers, to achieve better performance.

Techniques such as random search and Bayesian optimization can be used here.

Regularization: L1 and L2 regularization techniques can be used to avoid overfitting.

 Question #8: Can You Share a Challenging Problem You Encountered in a Machine Learning Project and How You Approached Solving it?

   Answer:

In a machine learning project where I was working on the development of a recommendation system for a streaming service, I faced the following challenge:

Initially, the data set had missing values and the data was scattered and contained noise.

This problem to be addressed required intensive data cleaning and preprocessing to ensure that the data was qualitative and sufficient for the development of the recommendation system.

Also, this service had a large number of users and the recommendation system had to efficiently manage a large volume of requests as best as possible.

At the same time, the recommendation system had to be unbiased towards different users and content categories.

To address these challenges, in addition to data cleaning, I used feature engineering to create new features that could effectively identify user behavior, such as the frequency of visits.

 Question #9: Describe a Project Where You Applied Machine Learning or AI Techniques to Analyze Time Series Data. What Challenges Did You Face?

   Answer:

A project in which I used machine learning techniques for the analysis of time-series data, concerned the prediction of future patterns of energy consumption.

The challenges I faced were the following:

Initially, time-series data contained noise and missing values.

So, to deal with this problem I applied data-cleaning techniques

Also, the time-series data naturally shows seasonal trends and for this, I had to identify and model these components to optimize the accuracy of the forecast.

Furthermore, choosing the right machine learning model for time-series forecasting was equally a challenge and I evaluated various models such as ARIMA and deep learning models such as LSTM and CNN.

 Ramping Up

​So, we've seen 9 key interview questions for the position of AI engineer, as well as the answers that fit well with each one.

This way, an AI engineer can have a better idea of the questions they might be asked in their next job interview so they can be properly prepared.

So, if you are intrigued and want to learn more about AI and data science in general, follow us and we’ll keep you updated with more educational articles!