These machine learning engineer interview questions have been co-created by instructors from FAANG companies. These questions reflect the expertise and firsthand experience of these instructors who also happen to be the hiring managers at top tech giants.

The **role of ML Engineers** is crucial as they drive organizational growth and improve the customers’ experience. With the growing demand for machine learning engineers, more and more tech professionals aspiring to enter this dynamic field.

As the competition intensifies, being well-prepared for interviews becomes crucial. So, help yourself with our specially curated list of machine learning questions that will not only help you understand the core concepts but also boost your confidence and give you a competitive edge.

These interview questions have been prepared for the most challenging interview scenarios. The instructors are familiar with the latest interview patterns at top companies like Google and Meta and hence they have compiled the list of ML Engineer interview questions accordingly.

So, just dive into the list of top 20 machine learning questions that you may not find everywhere.

**Also read: ****What is Machine Learning? A Comprehensive Guide**

## 1. What are the various types of ML algorithms?

The machine learning algorithms can broadly be classified into three types:

**Supervised learning: **These are trained using labeled data, meaning that their training is paired with a specific output label. It is mainly used in predictive tasks where the end goal is to learn by mapping inputs to output. It includes linear regression, support vector machine (SVM), neural networks, etc.**Unsupervised learning: **These involve unlabeled data and the end goal is to refer to the natural structure present within the data set. It is primarily used for clustering, association, and dimensionality reduction tasks. Its examples are K-means clustering, principal component analysis (PCA), Gaussian mixture models (GMM), etc.**Reinforcement learning: **In this, the algorithms learn by interacting with its environment, while receiving rewards or penalties based on their actions. These algorithms are used when the decision-making has to follow a sequence and the feedback is delayed. Its examples are Q-learning, deep Q-networks (DQN), etc.

## 2. What is your favorite algorithm? Is it used for classification or regression?

In this machine learning engineer interview question, the interviewer wants to test your understanding of machine learning algorithms. Random forest is my favorite ML algorithm. It is an ensemble learning method that functions by constructing multiple decision trees and outputting the mode of the classes (classification) and mean prediction (regression). I like it because:

- It can be used for both classification and regression tasks.
- It averages multiple decision trees and reduces overfitting, thereby improving the model’s performance on unseen data.
- It helps understand the importance of each feature in the prediction and helps understand the model better.

## 3. What are the different types of activation functions?

In this machine learning engineer interview question, the hiring manager wants to check your understanding of the various types of activation functions. In neural networks, the role of activation functions is critical because they provide non-linearity to the model, allowing it to learn several complex patterns. Some of the main activation functions are:

**Sigmoid: **It is commonly used in binary classification problems and outputs values between 0 and 1. It has a smooth gradient and outputs probabilities, while it is prone to vanishing gradient problems. **Tanh (Hyperbolic Tangent): **It is mostly used in the hidden layers of neural networks and outputs a value between -1 and 1. It is zero-centered helping in better convergence. But, its performance gets affected because of the vanishing gradient issues. **ReLU (Rectified Linear Unit):** It is used in CNNs and other deep learning models. It is highly efficient in making computations and helps mitigate the vanishing gradient problem. Its major disadvantage is that it can result in dead neurons during training.

**Also read: ****How to Become a Machine Learning Engineer in 2024?**

## 4. What is the expectation of a random variable?

The expected values of a random variable are a way to measure the center of the distribution. It is the long-run average value of the repetitions of the experiment that it represents. It is mathematically a discrete random variable with values and corresponding probabilities.

Understanding the expectations helps in areas like calculating the mean in probability distributions and forming the bias for variance and other such statistical measures.

## 5. Difference between MAE, RMSE, and the Mean of Distribution?

Through this machine learning engineer interview question, the interviewer wants to check if you know the differences between MAE, RMSE, and the Mean of Distribution.

Mean for Absolute Error (MAE) measures the average magnitude of the errors in a set of predictions. It is easy to understand and compute, while it is not sensitive to outliers. But, its major disadvantage is that it does not emphasize larger errors.

The Root Mean Squared Error (RMSE) is the square root of the average of squared differences between prediction and actual observations. Its key advantage is that it penalizes larger errors more than the smaller ones which helps in emphasizing the large errors. On the other hand, it is more sensitive to the outliers as compared to MAE.

The Mean of Distribution represents the central value of probability distribution.

MAE should be used to treat all the errors equally, while RSME should be used to penalize the larger errors.

## 6. When do we not use squared error? What difference does it make?

When dealing with outliers, the squared error might not be the best option. This happens because it disproportionately increases the errors for outliers. Using MAE can be better as it does not square the error term.

## 7. How do we perform Spam detection? What are the best metrics for this system: precision or recall?

In this machine learning engineer interview question, the interviewer wants to check how you detect spam and what is the best metric out of precision and recall.

To detect spam, techniques like Naive Bayes, SVMs, deep learning models, etc. can be used. There are two key methods for detecting spam - precision and recall.

Precision is the ratio of true positive predictions as compared to the predicted positives. High precision would mean that fewer legitimate emails have been marked as spam.

On the other hand, recall is the ratio of true positives to actual positives. If there is high recall then it would ensure that most spams are detected.

The choice between precision or recall depends on whether it is more important to avoid false positives or false negatives.

## 8. What is the bias-variance tradeoff? When can we use bagging/boosting to improve it?

In this machine learning engineer interview question, the hiring manager wants to check your understanding of the bias-variance tradeoff, and when should bagging or boosting be used to improve the bias-variance tradeoff.

**Bias-Variance Tradeoff**

The bias-variance tradeoff is a fundamental concept of machine learning and describes the tradeoff between two sources of errors affecting the performance of predictive models.

Bias is the error that happens due to overly simple models that cannot capture and understand the underlying patterns in data. If there is high bias, then it can lead to systematically inaccurate predictions.

On the other hand, the variance errors occur because the models are overly complex and they tend to capture noise within the training data or overfitting. High variance can lead to poorly unseen data in highly sensitive data.

**Bagging and Boosting**

Bagging and boosting are techniques that help in improving the performance of models by addressing the bias-variance tradeoff.

Bagging trains multiple models on different subsets of the training data and then averages their predictions to reduce the variance. It is most effective is models with high variance or overfitting.

Boosting, on the other hand, sequentially trains models reducing the bias. Here, each new model focuses on the errors of the previous model. It is to be used in models that have high bias or underfitting. Boosting helps reduce bias as well as improve efficiency.

**Also read: ****How to become Machine Learning Algorithm Engineer at Google?**

## 9. What is the stacking technique?

In this machine learning engineer interview question, the interviewer wants to assess your understanding of the stacking techniques.

Stacking is a learning technique where multiple models are trained at once. It combines their predictions using a meta-learner. Its primary aim is to make full use of the strengths of different models and to improve the overall performance. Here’s how it works:

**Train base learners:** It trains several base models on the training data**Generate meta features: **These base model’s predictions are the features for second-level models, also known as meta learners**Train meta-learners: **Final predictions are made based on the training of the meta-learners

## 10. Which regularisation technique results in sparse features? When to use L1, L2?

You can answer this machine learning engineer interview question by stating that the key role of regularization techniques is to prevent overfitting. This is done by adding penalties to the loss function based on the magnitude of the model parameters.

The L1 regularization (lasso) adds a penalty that is equal to the absolute value of the coefficients. It tends to produce sparse models with very few non-zero coefficients, thereby making it useful for feature selection.

While the L2 regularization adds a penalty that is equivalent to the square of the coefficients. Instead of producing sparse models, it shrinks the coefficients towards zero but very rarely to exact zero.

## 11. What is the difference between ML, DL, and AI?

In this machine learning engineer interview question, the interviewer wants to check if you know the differences between AI, ML, and DL.

Artificial intelligence (AI) consists of all the aspects that help create machines to perform tasks that traditionally require human intelligence. It includes domains like natural language processing (NLP), robotics, machine learning, computer vision, and more.

Machine learning (ML) is a subset of AI and focuses on developing algorithms that enable computers to learn and make data-based predictions or decisions.

On the other hand, deep learning (DL) is a part of ML and uses neural networks that have many deep layers with the capability of modeling complex patterns in large datasets. Image and speech recognition have been possible due to the advancements in DL.

## 12. What is k-means? What is the best value of k? How do you estimate it? How to measure performance here?

You can answer this machine learning interview question, you can state that k-means is an unsupervised learning algorithm that divides a dataset into k distinct, non-overlapping clusters based on similarity of features. The following steps are used in this algorithm:

- Initialization
- Assignment
- Update
- Repeat

The elbow method and silhouette score are some of the methods that can be used to determine the best value of k, while to measure its performance, inertia and silhouette score can be used.

## 13. How does LSTM handle vanishing gradient?

To answer this machine learning engineer interview question, you can say that the long-short term memory is a type of recurrent neural network (RNN) that is capable of handling the vanishing gradient problem. It handles the issue by developing and introducing a more complex unit structure that has gates to control the information flow. These gates are:

- Forget Gate
- Input Gate
- Cell State
- Output Gate

## 14. How would you find an anomaly in a distribution?

To detect an anomaly, rare items, events, or observations have to be identified. Its various techniques are:

**Statistical methods: **It detects anomalies based on statistical properties such as mean, standard deviation, etc. **Density-based methods:** DBSCAN and other such algorithms detect anomalies in the form of points in low-density regions.- Isolation forest: It is an ensemble method that has been specially designed and developed for detecting anomalies by isolating observations and randomly selecting a feature and splitting the data.

## 15. Elaborate on how you handle missing or corrupted data in a dataset

You can answer this machine learning engineer interview question, by stating that dropping those particular rows or columns or replacing them with other values is the best course of action to manage the situation of missing or corrupted data. In Pandas, there are two useful methods for this:

- IsNull() and dropna() to find the columns or rows with missing data
- Fillna() replaces the wrong values with a placeholder value

## 16. What is the confusion matrix in ML algorithms?

Answer this machine learning engineer interview question by stating that a confusion or error matrix is a table used to measure an ML algorithm’s performance. There are two parameters in it - actual and predicted. The confusion matrix is typically used in supervised learning, while in unsupervised learning it is called a matching matrix.

## 17. How do you prevent overfitting?

Overfitting is described as the situation wherein a model can learn the training data too well and even considers the random fluctuations in the training data as concepts and things to learn. As a result, it affects the model’s ability to generalize and apply it to new data.

To avoid overfitting, there are multiple ways, like:

- Regularization that involves the cost term for the features of the objective function
- Developing a simple model that has fewer variables and parameters
- K-fold and other cross-validation methods

## 18. What is cross-validation and why would you use it?

This is a commonly asked machine learning engineer interview question. Cross-validation is described as the statistical resampling of different parts of the dataset used to train and test a machine learning algorithm on different iterations.

It aims to assess the ability of a model to recognize a new set of data that was not used for training. It helps avoid overfitting of the data.

## 19. What assumptions do you make before starting linear regression?

To answer this machine learning engineer interview question, you can say “I make five assumptions before starting with a linear regression model. They are:

- Linear relationship
- No auto-correlation
- Homoscedasticity
- Multivariate normality
- No or little multicollinearity”

## 20. Explain the decision tree classification

You can answer this machine learning engineer interview question, by stating that when a tree-like structure is used to generate any regression or classification models, then it is known as a decision tree. While it is being developed, the data set is split into smaller subsets with branches and nodes, just like in a tree. The decision tree handles both category and numerical data.

## How Interview Kickstart can help you clear the Machine Learning Engineer Interview?

In this competitive field of machine learning, cracking the interview and becoming an ML engineer is a very challenging task. They need to have a strong understanding of the technical concepts and other soft skills like problem-solving, communication, collaboration, etc. Interview Kickstart’s machine learning interview masterclass is designed to help aspiring engineers and tech professionals prepare for and succeed in rigorous technical interviews.

It is designed and taught by FAANG+ engineers and industry experts to help you crack even the toughest of interviews at leading tech and tier-1 companies. With this, you will learn data structures and algorithms, system design, supervised & unsupervised learning along with deep learning.

Enroll now to learn how to optimize your LinkedIn profile, build ATS-clearing resumes, personal branding, and more.

Watch this mock interview to learn more about the different types of machine learning interview questions and how you can answer them to not only leave a good impression, but also to clear the interviews.

VIDEO

### FAQs: Machine Learning Engineer Interview Questions

**What kind of projects should I include in my portfolio to impress ML interviewers?**

Including a variety of projects that demonstrate your breadth and depth in machine learning is key. Aim for projects that highlight:

**Diverse applications: **Projects that span different domains such as NLP, computer vision, time-series forecasting, and reinforcement learning.**Complexity: **Include projects that tackle complex problems, showcase your ability to handle large datasets, and use advanced algorithms.**End-to-end solutions: **Demonstrate your ability to build complete solutions, from data preprocessing and feature engineering to model deployment and monitoring.

**How important are coding skills in a machine learning engineer interview?**

Coding skills are crucial for machine learning engineer roles. Interviewers often assess your ability to:

**Implement algorithms: **Write efficient and correct implementations of ML algorithms from scratch.**Data manipulation:** Use libraries like Pandas, NumPy, and Scikit-Learn for data processing.**Model development:** Build and fine-tune machine learning models using frameworks such as TensorFlow, PyTorch, or Keras.

**What are some common mistakes to avoid during an ML interview?**

Some common mistakes to avoid include:

**Overfitting to interview prep: **Relying too heavily on memorized solutions rather than understanding fundamental concepts.**Ignoring edge cases:** Failing to consider edge cases and robustness in your solutions.**Lack of clarity:** Not clearly explaining your thought process and decisions during problem-solving.

**How can I effectively communicate my ML project experience during an interview?**

To effectively communicate your project experience:

**Structured approach:** Use the STAR method (Situation, Task, Action, Result) to clearly outline your contributions and the impact of your work.**Technical depth:** Explain the technical challenges you faced, the algorithms you used, and why you chose them.**Results and impact:** Highlight the outcomes and how your project added value, such as improved accuracy, efficiency, or insights gained.

**Related articles:**