Top 90+ Machine Learning Interview Questions with AWS & Azure Insights in 2025

1. Introduction to Machine Learning and Core Concepts
2. Leading Machine Learning Algorithms and Tools
   • Machine Learning Interview Questions and Answers
   • Machine Learning Interview Questions Based on Programming Fundamentals 
   • Machine Learning Interview Questions for Azure
   • Machine Learning Interview Questions for AWS
   • Role-Specific Open-ended Machine Learning Interview Questions 
   • Machine Learning Interview Questions Asked at Amazon 
   • Machine Learning Interview Questions Asked at Baidu
   • Machine Learning Interview Questions Asked at Spotify 
   • Machine Learning Interview Questions Asked at Capital One
3. Conclusion

Introduction to Machine Learning and Core Concepts

Machine learning is a key part of modern data science, where systems learn and improve from experience without needing direct programming. It includes techniques like supervised, unsupervised, reinforcement, and deep learning. Each technique has its role in analyzing data and making predictions.

Leading Machine Learning Algorithms and Tools

Before diving into the interview questions, let’s explore some of the fundamental machine-learning algorithms and tools widely used in the industry today:

• Supervised Learning Algorithms: These include methods like linear regression, logistic regression, support vector machines (SVM), decision trees, and random forests. They are used when the machine learns from labeled data to make predictions.
• Unsupervised Learning Algorithms: These cover techniques such as K-means clustering, hierarchical clustering, and association rule learning. They are used to find patterns and relationships in data without labeled outcomes.

• Deep Learning Architectures: This involves advanced models like convolutional neural networks (CNNs) used in image recognition, recurrent neural networks (RNNs) for analyzing sequences, and transformer models like BERT, which excel at processing natural language tasks. These models are especially powerful for handling complex data patterns and tasks.

Machine Learning Interview Questions and Answers ^

Q1) What is the difference between inductive and deductive machine learning?

Ans) Inductive machine learning involves learning patterns and generalizing from specific examples or data instances. The model is trained on a dataset and infers rules or patterns from this data to make predictions. For instance, suppose you have a dataset of emails labeled as “spam” or “not spam.” An inductive learning algorithm will learn patterns from this labeled dataset and create a model to predict whether new emails are spam based on these learned patterns. Essentially, it starts with specific observations and moves towards broader generalizations.

Ans) On the other hand, deductive machine learning involves applying general rules or principles to specific cases to derive conclusions. It begins with a general rule and uses it to make predictions about new data. For example, if you have a set of rules that define what constitutes a “spam” email (e.g., emails containing certain keywords are considered spam), a deductive learning system would apply these rules to classify new emails as spam or not. In this case, the model uses predefined rules to evaluate specific instances, starting from a general principle and working towards a specific conclusion.

Q2) Why is Naïve Bayes machine learning algorithm naïve?

Ans) Naïve Bayes is termed “naïve” because it assumes that all features in the dataset are independent of each other given the class. This assumption simplifies the computation significantly, as it allows the model to consider each feature’s contribution towards the probability of the class independently. However, in real-world data, features often exhibit some level of correlation, making this assumption a simplification that may not always hold. Despite this, Naïve Bayes can perform remarkably well due to its robustness and efficiency, especially in large datasets.

Q3) You are given a dataset where the number of variables (p) is greater than the number of observations (n) (p>n). Which is the best technique to use and why?

Ans) When dealing with datasets where the number of variables (p) exceeds the number of observations (n), techniques like Lasso and Ridge regression are particularly effective. These methods are known as shrinkage methods or regularization techniques. Lasso (Least Absolute Shrinkage and Selection Operator) helps in feature selection by reducing some coefficients to zero, thus performing variable selection. Ridge regression, on the other hand, reduces the complexity of the model by imposing a penalty on the size of the coefficients. Both methods help prevent overfitting by shrinking the coefficients, but the choice between them depends on the specific characteristics of the data and the desired outcome (feature selection vs. coefficient shrinkage).

Q4) When will you use classification over regression?

Ans) Classification and regression are both types of supervised learning techniques but are used for different types of predictive modeling problems. Classification is used when the output variable is categorical, which means it is used to predict discrete labels (e.g., spam or not spam, malignant or benign tumor). On the other hand, regression is used for predicting a continuous quantity (e.g., price of a house, temperature tomorrow). Thus, you would use classification when you need to determine distinct categories within your data and regression when predicting a continuous outcome.

Q5) Consider two classes, A1 and A2, whose features are generated using a Gaussian function. The feature variables in class A1 correlate to 0.5, while those in class A2 correlate to -0.5. Which of the following methods would you prefer for classifying this dataset: KNN, Linear Regression, QDA, LDA, or KNN?

Ans) For datasets where the features are Gaussian-distributed and classes exhibit different covariance structures, Quadratic Discriminant Analysis (QDA) is particularly suitable. QDA allows for each class to have its covariance matrix, which makes it more flexible in capturing the differences between classes when these differences are expressed in the variance and correlation of the features. This flexibility makes QDA appropriate for scenarios where the decision boundary is expected to be non-linear and where classes are not well-separated by linear combinations of features, as might be assumed in Linear Discriminant Analysis (LDA).

Q6) What are the two best-known regularization techniques?

Ans) The two most common regularization methods are ridge regression and lasso regression.

Machine Learning Interview Questions Based on Programming Fundamentals ^

Q7) How will you find the middle element of a linked list in a single pass?

Ans) Finding the middle element of a linked list in a single pass involves using two-pointers, not traversing the list twice. This method is efficient as it completes the task in just one pass through the list. Here’s how you can achieve this:

Steps: 

1. 1. Initialize two pointers: Begin with both a slow pointer (slw_ptr) and a fast pointer (fst_ptr) at the start of the list.
   2. Advance the pointers: Move the fast pointer (fst_ptr) two steps at a time, and the slow pointer (slw_ptr) one step at a time.
   3. Determine the middle: Continue advancing the pointers until the fast pointer reaches the end of the list. At this point, the slow pointer will be positioned at the middle element of the list.

This method ensures an efficient and accurate determination of the middle element in just one pass through the linked list.

Q8) Write code to print the InOrder traversal of a tree.

Ans) The following function will output the InOrder traversal of a tree in C++:

void printInorder(struct Node* node)
{
    if (node == NULL)

        return;

    printInorder(node->left);

    cout << node->data << " ";

    printInorder(node->right);

}

Machine Learning Interview Questions for Azure ^

Q9) What are the key components of Azure Machine Learning service?

Ans) The key components include 

• • Azure ML Studio: An interface for building and deploying models.
  • Compute Targets: Resources like VMs or AKS for running models.
  • Azure ML Pipelines: For automating workflows.
  • Datasets: Versioned data storage for consistent use in experiments.
  • Model Registry: A repository for managing and deploying models.

Q10) Explain the concept of MLOps in Azure Machine Learning.

Ans) MLOps in Azure ML is about automating and managing the end-to-end machine learning lifecycle. It uses Azure DevOps and ML Pipelines to continuously integrate, deploy, and monitor models in production, ensuring they perform well and are up-to-date with new data.

Q11)What is the role of Azure Cognitive Services in machine learning?

Ans) Azure Cognitive Services provide ready-made AI capabilities, like image recognition and language understanding, that can be integrated into machine learning models. They help add advanced features to applications without the need to build these models from scratch.

Machine Learning Interview Questions for AWS ^

Q12)How does SageMaker Ground Truth help with data labeling?

Ans) SageMaker Ground Truth simplifies the data labeling process by offering a combination of human labeling and machine-assisted labeling techniques. It uses human labelers for initial labeling tasks and then leverages automatic labeling and active learning to improve efficiency and reduce costs. This results in highly accurate training datasets that are essential for building reliable machine-learning models. Ground Truth also integrates with S3 and provides real-time monitoring of the labeling process.

Q13)How do you integrate AWS Glue with machine learning workflows?

Ans) AWS Glue is an ETL (Extract, Transform, Load) service that can be integrated into machine learning workflows to prepare and transform data before it is used for training models. Glue simplifies the process of cleaning and structuring large datasets, making them ready for analysis and model training. By using Glue, you can automate data preparation tasks, ensuring that your machine-learning models are trained on high-quality, well-organized data. Glue also integrates with SageMaker, allowing you to directly pass prepared data into your training jobs.

Role-Specific Open-ended Machine Learning Interview Questions ^

Q14) What will you do if training results in very low accuracy?

Ans) Low accuracy might indicate issues such as insufficient model complexity, poor feature selection, inadequate data quality, or a need for more training examples. To address this, one could:

• Increase the complexity of the model or try a different algorithm better suited to the problem’s complexity.
• Perform feature engineering to include more relevant features.
• Clean the data to remove noise and correct errors.
• Use techniques like data augmentation or synthetic data generation to increase the amount of training data.

Q15) Which is your favorite machine learning algorithm? Why it is your favorite and how will you explain that machine learning algorithm to a layperson?

Ans) The Random Forest algorithm is highly favored due to its versatility and power. It is suitable for both classification and regression tasks and performs well on large datasets, managing both numerical and categorical data effectively. To simplify for someone unfamiliar with technical jargon, one could describe it as a method where multiple decision trees (similar to students in a classroom) each learn to make predictions in slightly different ways. When a decision is required, all the trees contribute their ‘votes’ towards the outcome, and the most common answer is selected as the final result. This collective approach, likened to a “crowd of students,” renders the Random Forest algorithm very effective and less error-prone compared to relying on any single decision tree.

Machine Learning Interview Questions Asked at Amazon ^

Q16) How will you weigh 9 marbles 3 times on a balance scale to find the heaviest one?

Ans) Assume all the marbles are identical in appearance and weight, except for one that is slightly heavier.

Here’s how to find the heavier marble:

1. Divide the 9 marbles into three groups of three.
2. Use a balance scale to compare the weights of two groups. If the scale balances, the heavier marble is in the group not weighed. If the scale tips, the heavier marble is in the heavier group.
3. Once you identify the group with the heavier marble, repeat the process with the three marbles in that group to find the heaviest one

Q17) Why is gradient checking important?

Q18)  Which one is better – random weight assignment or assigning the same weights to the units in the hidden layer?

Ans) For hidden layers of a neural network, it is better to assign random weights to each unit of the layer than to assign the same weights to it. That is because if we use the same weights for each unit, then all the units will generate the same output and lower the entropy. Thus, we should always use random weights that can break the symmetry and quickly reach the cost function minimal.

Q19) Explain the difference between MLE and MAP inference.

Ans) Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) inference are two common methods in statistical estimation:

MLE (Maximum Likelihood Estimation): This method seeks the parameter values that maximize the likelihood function, which measures how likely the observed data are given a particular set of model parameters. MLE does not incorporate any prior knowledge or assumptions about the parameters.

MAP (Maximum A Posteriori): MAP inference, on the other hand, incorporates prior knowledge or beliefs about the parameters into the estimation process. It seeks the parameter values that maximize the posterior probability, which combines the likelihood of the data with the prior probability distribution of the parameters.

Machine Learning Interview Questions Asked at Baidu ^

Q20) What are the reasons for gradient descent to converge slowly or not converge in various machine learning algorithms?

Ans) Here are some reasons why the gradient descent algorithm might show slow convergence or fail to converge:

• The cost function may not be convex.
• An improper learning rate was chosen initially. If the learning rate is too high, the steps may oscillate, preventing the global minimum from being reached. Conversely, if the learning rate is too low, the algorithm may take an excessively long time to reach the global minimum.

Q21) Given an objective function, calculate the range of its learning rate.

Ans) To determine the optimal range for an objective function’s learning rate, you can start by training a network with a low initial learning rate and then increase the learning rate exponentially with each batch. Record the loss values associated with each learning rate and plot them. This will help you visualize the learning rate range that leads to a rapid decrease in the loss function.

Machine Learning Interview Questions Asked at Spotify ^

Q22) Explain BFS (Breadth First Search algorithm)

Ans) Breadth First Search (BFS) is a graph traversal algorithm that starts at a chosen node (root) and explores all its neighbors at the present depth before moving on to nodes at the next depth level. It uses a queue to keep track of the next location to visit. BFS is used to find the shortest path on unweighted graphs, searching layer by layer outward from the root. It’s particularly useful in scenarios where you need to find the shortest path from the initial to the target node.

Q23) How will you tell if a song in our catalog is a duplicate or not?

Ans) To detect duplicates in a music catalog, you can use a combination of metadata comparison and audio fingerprinting:
Metadata Comparison: Check attributes like track name, artist, album, and duration. Exact matches on these fields can suggest duplicates.
Audio Fingerprinting: Use algorithms to generate a unique digital “fingerprint” based on the audio file’s content. Songs with identical fingerprints are considered duplicates. Tools like AcoustID can automate this process effectively.

Machine Learning Interview Questions Asked at Capital One ^

Q24) Differentiate between gradient-boosted tree and random forest machine learning algorithm.

Ans) 

• Random Forest: An ensemble method that builds multiple decision trees (forest) and outputs the mode of the classifications (majority voting) or mean prediction (average) of the individual trees. It reduces variance and prevents overfitting by using bagging (bootstrap aggregating) to create each tree on a different subset of the data.
• Gradient Boosting Trees: Another ensemble technique that builds trees sequentially: each new tree is built to correct the errors made by the previously built trees. Unlike random forests, which build each tree independently, gradient boosting uses a gradient descent algorithm to minimize errors in a predictive model by optimizing a loss function.

Q25) Considering that you have 100 data points and you have to predict the gender of a customer. What are the difficulties that could arise?

Ans) With only 100 data points, several challenges could compromise the effectiveness of a predictive model:

• Overfitting: With such a small dataset, there is a high risk of overfitting, where the model learns the noise in the training data instead of generalizing from patterns.
• Limited Representation: With few data points, the diversity of the sample may be insufficient to represent the broader population accurately.
• High Variance: Models trained on small datasets are generally less robust and can exhibit high variance, leading to inconsistent predictions on new, unseen data.
• Feature Limitations: Limited data restricts the complexity of the model and the number of features that can be used without exacerbating overfitting.

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Conclusion

Related References

• Top 100+ Data Modelling Interview Questions for Data Professionals
• The Role of AI and ML in Cloud Computing
• Azure Data Factory For Beginners
• Azure AI/ML Certifications: Everything You Need to Know
• AI-900 Hands-On Lab
• AI-102 Hands-On Lab
• DP-100 Hands-On Lab

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