100+ Machine Learning Engineer Interview Questions and Answers

The task is to find a permutation array of size 2*N with specific conditions.

• Create an array of size 2*N to store the permutation.

• Ensure each number from 1 to N appears exactly twice in the array.

• Check that the distance between the second and first occurrence of each number is equal to the number itself.

• Return the array if conditions are met, else return an empty array.

Given an array of integers representing digits of a number, swap two values to form the maximum possible number.

• Iterate through the array to find the maximum digit.

• Swap the maximum digit with the first digit if it is not already at the first position.

• Handle cases where there are multiple occurrences of the maximum digit.

Given an array of positive integers and an integer K, determine if there exists a subset whose sum equals K.

• Use dynamic programming to solve this problem efficiently

• Create a 2D array to store if a subset sum is possible for each element and target sum

• Iterate through the array and update the 2D array based on current element and target sum

• Check if the last element of the 2D array is true for the given target sum

Find all possible paths from top-left to bottom-right in a matrix by moving only right or down.

• Use backtracking to explore all possible paths from top-left to bottom-right in the matrix

• At each cell, recursively explore moving right and down until reaching the bottom-right corner

• Keep track of the current path and add it to the result when reaching the destination

Python uses automatic memory management through garbage collection.

• Python uses reference counting to keep track of object references.

• When an object's reference count reaches zero, it is automatically deallocated.

• Python also employs a garbage collector to handle cyclic references.

• The 'gc' module provides control over the garbage collector.

• Memory management can be optimized using techniques like object pooling and memory profiling.

Different types of learning include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning.

• Supervised learning: Training a model using labeled data to make predictions or classifications.

• Unsupervised learning: Training a model on unlabeled data to discover patterns or relationships.

• Semi-supervised learning: Combining labeled and unlabeled data for training.

• Reinforcement learning: Training a model to make decisions b...read more

Steps to create a document parsing API with chunk storage and retrieval.

• 1. Define API endpoints: Create a POST endpoint for document upload and a GET endpoint for chunk retrieval.

• 2. Document parsing: Use libraries like PyPDF2 or Tesseract for extracting text from documents.

• 3. Chunking: Implement logic to split the extracted text into manageable chunks, e.g., sentences or paragraphs.

• 4. Database setup: Choose a database (e.g., PostgreSQL, MongoDB) to store the chunks with relev...read more

Yes, I once had difficulty managing multiple vendors for data collection in a machine learning project.

• Coordinating timelines and deliverables with multiple vendors

• Ensuring data quality and consistency across different sources

• Resolving conflicts or discrepancies in data provided by vendors

Address concerns, communicate benefits, involve stakeholders, provide training, monitor progress

• Address concerns and listen to feedback from associates to understand their perspective

• Communicate the benefits of the process change and how it will improve efficiency or outcomes

• Involve stakeholders in the decision-making process to gain their support and input

• Provide training and resources to help associates adapt to the new process

• Monitor progress and gather feedback to make ad...read more

Explanation of evaluation metrics, precision, recall, entropy, Gini impurity, bagging, boosting, validation vs test data, LSTM, GRU, K-means clustering, and importing CSV datasets.

• Evaluation metrics: used to measure the performance of machine learning models (e.g., accuracy, precision, recall, F1 score)

• Precision: ratio of true positive predictions to the total predicted positives (TP / (TP + FP))

• Recall: ratio of true positive predictions to the total actual positives (TP / (T...read more

ML algorithms are techniques used to train models to make predictions or decisions based on data.

• Supervised learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors

• Unsupervised learning algorithms: K-means clustering, hierarchical clustering, principal component analysis

• Reinforcement learning algorithms: Q-learning, SARSA

• Deep learning algorithms: Convolutional neural networks, recurrent neural ne...read more

A docker image is a lightweight, standalone, executable package that includes everything needed to run an application.

• Docker images are created using a Dockerfile which contains instructions for building the image.

• Images can be stored in a registry and pulled to run on any machine with Docker installed.

• To check running containers, use the command 'docker ps' which lists all running containers.

• To see all containers, including stopped ones, use 'docker ps -a'.

Preprocess image data while maintaining ratios and register multiple models in Django.

• Resize images while maintaining aspect ratio using libraries like PIL or OpenCV

• Normalize pixel values to a range of 0-1 for better model performance

• Augment data using techniques like rotation, flipping, or cropping to increase dataset size

• Use data generators in Keras to efficiently load and preprocess images in batches

• Register multiple models in Django by creating separate model classes in m...read more

AWS S3 has 6 storage classes: S3 Standard, S3 Intelligent-Tiering, S3 Standard-Infrequent Access, S3 One Zone-Infrequent Access, S3 Glacier, and S3 Glacier Deep Archive.

• S3 Standard: for frequently accessed data

• S3 Intelligent-Tiering: automatically moves data to the most cost-effective tier

• S3 Standard-Infrequent Access: for long-lived, infrequently accessed data

• S3 One Zone-Infrequent Access: for infrequently accessed data that can be recreated

• S3 Glacier: for long-term archival...read more

Design a system to extract insights from customer service chat data

• Implement natural language processing (NLP) techniques to analyze text data

• Use sentiment analysis to understand customer emotions and satisfaction levels

• Identify frequently asked questions or common issues to improve customer service

• Create visualizations to present key insights and trends to stakeholders

The model is not working due to overfitting, lack of data, and incorrect features. Steps to improve include regularization, data augmentation, and feature engineering.

• Overfitting: Model may be too complex for the data, use regularization techniques like L1/L2 regularization or dropout.

• Lack of data: Collect more data or use data augmentation techniques like flipping, rotating, or adding noise to existing data.

• Incorrect features: Perform feature engineering to create more relev...read more

Creating a DataFrame from the Iris dataset and extracting columns for visualization.

• Import necessary libraries: `import pandas as pd` and `import seaborn as sns`.

• Load the Iris dataset: `iris = sns.load_dataset('iris')`.

• Create a DataFrame: `df = pd.DataFrame(iris)`.

• Extract common X and Y columns for visualization: `X = df[['sepal_length', 'sepal_width']]` and `Y = df['species']`.

• Use visualization libraries like Matplotlib or Seaborn to plot: `sns.scatterplot(x='sepal_length', ...read more

Common evaluation metrics for ML models include accuracy, precision, recall, F1-score, and AUC-ROC, implemented using libraries like scikit-learn.

• Accuracy: Measures the proportion of correct predictions. Example: accuracy_score(y_true, y_pred).

• Precision: Indicates the accuracy of positive predictions. Example: precision_score(y_true, y_pred).

• Recall: Measures the ability to find all relevant instances. Example: recall_score(y_true, y_pred).

• F1-Score: Harmonic mean of precision ...read more

Python function to check if a number is palindrome or not.

• Convert the number to a string

• Reverse the string

• Compare the reversed string with the original string

• Return True if they are equal, else False

Use natural language processing techniques to detect and identify medicines on a doctor's prescription.

• Preprocess the text by removing noise and irrelevant information.

• Tokenize the text to break it down into individual words or characters.

• Use a language model or dictionary to match the tokens with known medicines.

• Apply machine learning algorithms like NER (Named Entity Recognition) to identify medicine names.

• Consider context and surrounding words to improve accuracy of predic...read more

Metrics for regression problems

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• Mean Absolute Error (MAE)

• R-squared (R²) score

• Coefficient of Determination

• Explained Variance Score

Python function to check if a number is prime or not

• Check if number is less than 2, return False

• Check if number is divisible by any number from 2 to its square root, return False

• Else, return True

To deal with skewed distribution of a variable, transformations like log, square root, or box-cox can be applied.

• Apply log transformation to reduce right skewness

• Apply square root transformation to reduce left skewness

• Apply box-cox transformation for a more generalized approach

• Consider removing outliers before applying transformations

Designed a feed ranking system using collaborative filtering and content-based filtering techniques.

• Utilized collaborative filtering to recommend items based on user behavior and preferences.

• Incorporated content-based filtering to recommend items based on their attributes and characteristics.

• Implemented a hybrid approach combining collaborative and content-based filtering for improved recommendations.

• Used machine learning algorithms such as matrix factorization, k-nearest nei...read more

Chose Yolo for its real-time processing speed and simplicity compared to two-stage detectors.

• Yolo is faster than two-stage detectors as it processes the image in a single pass

• Yolo is simpler to implement and train compared to two-stage detectors like Faster R-CNN

• Yolo is more suitable for real-time applications where speed is crucial, such as autonomous driving or video surveillance

Variance in principal component analysis helps to identify the most important features in the data.

• Variance measures the spread of data points around the mean, indicating the importance of each feature in capturing the overall variability.

• Higher variance implies more information is retained by the principal components, making them more significant in representing the data.

• By selecting components with high variance, we can reduce the dimensionality of the data while preserving...read more

Null hypothesis is a statement that assumes no relationship or difference between variables. P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true.

• Null hypothesis is a statement that assumes no effect or relationship between variables

• P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true

• Null hypothesis is typically denoted as H0, while an alternative ...read more