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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

50+ MongoDB Interview Questions and Answers

J
Janvi Kumari Last Updated : 19 Jul, 2024
8 min read

Introduction

MongoDB is a NoSQL database offering high performance and scalability. It stores data as documents, similar to JSON objects, allowing for complex structures like nested documents and arrays. This approach matches native data types in many programming languages. It also reduces the need for joins with embedded documents and arrays. Moreover, it supports dynamic schema for flexible data modeling. These features make MongoDB a robust choice for modern data management and an important topic for interviews. In this article we will see some of the most common interview questions on MongoDB.

Mongodb interview questions

MongoDB Interview Questions and Answers

Here are over 50 most commonly asked interview questions on MongoDB.

Q1. What is MongoDB?

Answer: MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents called BSON (Binary JSON). It’s designed for scalability, performance, and high availability.

Q2. What are documents and collections in MongoDB?

Answer: Documents are basic units of data in MongoDB, similar to rows in relational databases. They’re JSON-like objects with field-value pairs. Collections are groups of documents, analogous to tables in relational databases.

Q3. What are the differences between MongoDB and traditional relational databases?

Answer: Here are 4 main differences between the two:

  1. Data model: MongoDB uses flexible documents; relational DBs use fixed schemas.
  2. Scalability: MongoDB offers easier horizontal scaling.
  3. Querying: MongoDB uses a document-based query language; relational DBs use SQL.
  4. Relationships: MongoDB typically denormalizes data; relational DBs use joins.

Q4. What are the types of NoSQL databases?

Answer: There are four main types of NoSQL databases:

  1. Document Database: Stores data in documents similar to JSON. Examples include MongoDB, CosmosDB, and CouchDB.
  2. Key-Value Database: Stores data as a collection of key-value pairs. Examples include Redis and DynamoDB.
  3. Column Store Database: Organizes data in columns within a keyspace. Examples include Cassandra, BigTable, and HBase.
  4. Graph Store Database: Designed for data best represented as graphs with interconnected nodes. Examples include AllegroDB and GraphDB.

Q5. What are the advantages of MongoDB?

Answer: MongoDB offers several benefits:

  • Handles large volumes of data efficiently.
  • Supports cross-platform operations.
  • Provides high performance and easy scalability.
  • Eliminates the need for complex joins.
  • Supports horizontal and vertical scaling.
  • Available on cloud platforms like Azure and AWS.

Q6. What is sharding in MongoDB?

Answer: Sharding is MongoDB’s method for distributing data across multiple machines. It’s used to support deployments with very large data sets and high throughput operations.

Q7. Describe indexing in MongoDB.

Answer: Indexing in MongoDB improves query performance by creating efficient data structures for faster data retrieval. MongoDB supports various index types including single field, compound, multikey, and text indexes.

Q8. What are replica sets in MongoDB?

Answer: Replica sets are a group of MongoDB servers that maintain the same data set, providing redundancy and high availability. They consist of primary and secondary nodes, with automatic failover.

Q9. Explain the aggregation framework in MongoDB.

Answer: The aggregation framework is a set of analytical tools for processing data and returning computed results. It uses a pipeline concept where operators transform documents as they pass through.

Q10. How does MongoDB ensure data consistency?

Answer: MongoDB ensures consistency through reading and writing concerns, journaling, and two-phase commits for multi-document transactions.

mongodb interview questions

Q11. What are capped collections in MongoDB?

Answer: Capped collections are fixed-size collections that maintain insertion order and automatically overwrite old documents when the size limit is reached. They’re useful for log files or caching.

Q12. Describe the $lookup operator in MongoDB.

Answer: The $lookup operator performs a left outer join to another collection in the same database. It’s used in aggregation pipelines to combine data from multiple collections.

Q13. What is the ObjectId in MongoDB?

Answer: ObjectId is a 12-byte unique identifier automatically generated for each document in MongoDB if not specified by the user. It consists of a timestamp, machine identifier, process ID, and a random value.

Q14. Explain the concept of “schema-less” in MongoDB.

Answer: MongoDB is schema-less, meaning documents in the same collection can have different fields. This flexibility allows for easy adaptation to changing data requirements without needing to modify a rigid schema.

Q15. What is the difference between save() and insert() methods?

Answer: insert() adds a new document to the collection.
save() inserts a new document if it doesn’t exist, or updates an existing one if the _id matches.

Q16. How does MongoDB ensure high availability?

Answer: MongoDB ensures high availability through replica sets, automatic failover, data redundancy across multiple servers, and read scaling through secondary reads.

Q17. What is the purpose of the explain() method in MongoDB?

Answer: The explain() method provides information about query execution plans. It’s used for query optimization and understanding how MongoDB executes queries.

Q18. Describe MongoDB’s storage engines.

Answer: MongoDB supports multiple storage engines:

  • WiredTiger: The default since version 3.2, offering better performance and compression.
  • MMAPv1: The original engine, deprecated in newer versions.
  • In-Memory: For faster operations when persistence isn’t required.

Q19. How does MongoDB handle transactions?

Answer: Since version 4.0, MongoDB supports multi-document ACID transactions. They can be used across multiple operations, collections, and databases.

Q20. What is the difference between $where and $expr in MongoDB queries?

Answer: $where allows JavaScript expressions in queries but is slower and less secure.
$expr allows the use of aggregation expressions within the query language, offering better performance and security.

Q21. Explain the concept of TTL (Time-To-Live) indexes in MongoDB.

Answer: TTL indexes automatically remove documents from a collection after a specified amount of time or at a specific clock time. They’re useful for managing data like sessions, logs, or temporary data.

Q22. What is BSON in MongoDB?

Answer: BSON (Binary JSON) is the binary-encoded serialization of JSON-like documents that MongoDB uses for document storage and data transfer.

Q23. How do you create a database in MongoDB?

Answer: You can create a database using the command use database_name. If it doesn’t exist, MongoDB creates it when you first store data.

Q24. What is the significance of the _id field in MongoDB documents?

Answer: The _id field serves as a primary key for MongoDB documents. It’s automatically added if not specified and must be unique within a collection.

Q25. Explain the difference between $set and $unset operators.

Answer: The $set operator in MongoDB updates existing fields or adds new ones to a document. In contrast, the $unset operator removes specified fields from a document. $set modifies or creates fields, whereas $unset deletes fields entirely.

Q26. What is the purpose of the $push operator in MongoDB?

Answer: $push adds an element to an array field in a document. If the field doesn’t exist, it creates a new array with the element.

Q27. Explain the concept of covered queries in MongoDB.

Answer: A covered query is one where all fields in the query are part of an index, so MongoDB can return results without examining any documents.

Q28. What is the purpose of the $lookup stage in aggregation?

Answer: $lookup performs a left outer join with another collection in the same database, allowing data combination in aggregation pipelines.

Q29. How does MongoDB handle map-reduce operations?

Answer: MongoDB supports map-reduce operations for complex data processing, though aggregation pipeline is now preferred for most use cases.

Q30. What is the difference between $ and $all operators?

Answer: $and performs a logical AND operation on an array of expressions.
$all matches arrays that contain all elements specified in the query.

Interview Questions

Q31. How can you optimize MongoDB query performance?

Answer: Optimize through proper indexing, using covered queries, avoiding large documents, and using the explain() method to analyze query execution.

Q32. What is the purpose of the $match stage in aggregation?

Answer: $match filters documents to pass only those that match specified conditions to the next pipeline stage.

Q33. Explain the concept of write concerns in MongoDB.

Answer: Write concerns determine the level of acknowledgment requested from MongoDB for write operations, affecting data durability and latency.

Q34. What is the difference between $inc and $mul operators?

Answer: $inc increments a field by a specified value.
$mul multiplies a field by a specified value.

Q35. How does MongoDB handle full-text search?

Answer: MongoDB supports full-text search through text indexes and the $text operator for efficient text-based queries.

Q36. What is the purpose of the $group stage in aggregation?

Answer: $group groups documents by a specified expression and can perform aggregation operations on grouped data.

Q37. Explain the concept of change streams in MongoDB.

Answer: Change streams allow applications to watch for changes in the database in real-time, useful for triggering events or keeping caches updated.

Q38. How to add data in MongoDB?

Answer: Use the insertOne method for single documents and insertMany for multiple documents.

db.books.insertOne({"title": "ScholarHat"})
db.books.insertMany([{"title": "Book1"}, {"title": "Book2"}])

Q39. How can you enforce document validation in MongoDB?

Answer: Use schema validation rules with the $jsonSchema operator to enforce document structure and field types within a collection.

Q40. What is the difference between $elemMatch and $all in MongoDB queries?

Answer: The $elemMatch operator matches documents containing an array field with at least one element that meets all specified criteria. In contrast, the $all operator matches documents where an array field contains all specified elements, regardless of order.

Q41. Explain the Replication Architecture in MongoDB.

Answer: The primary node handles write operations and logs changes in the oplog, which secondary nodes replicate to maintain data consistency.

Q42. What is the purpose of the $out stage in aggregation pipelines?

Answer: The $out stage takes the documents returned by the aggregation pipeline and writes them to a specified collection, either creating a new collection or replacing an existing one.

Q43. Explain the concept of read preferences in MongoDB.

Answer: Read preferences determine how MongoDB clients direct read operations to members of a replica set, allowing for customization of read distribution for performance and reliability.

Q44. What is the difference between $unwind and $flatten in MongoDB?

Answer: The $unwind operator deconstructs an array field from input documents, outputting one document per array element. In contrast, $flatten (introduced in MongoDB 5.0) flattens nested arrays within a document without creating separate documents for each element.

Q45. How does MongoDB handle concurrency control?

Answer: MongoDB uses multi-version concurrency control (MVCC) to allow concurrent reads and writes. Write operations acquire exclusive locks, while read operations use shared locks.

Q46. What is the purpose of the $graphLookup stage in aggregation?

Answer: $graphLookup performs a recursive search on a collection, useful for hierarchical data structures or graph-like data models.

Q47. Explain the concept of the oplog in MongoDB.

Answer: The oplog (operations log) is a special capped collection that keeps a rolling record of all operations that modify data in the database, crucial for replication in MongoDB.

Q48. How can you implement soft deletes in MongoDB?

Answer: Soft deletes can be implemented by adding a boolean field (e.g., ‘isDeleted’) to documents and updating it instead of removing the document, then filtering queries to exclude “deleted” documents.

Q49. What is the purpose of the $merge stage in aggregation pipelines?

Answer: The $merge stage writes the results of an aggregation pipeline to a collection, allowing for sophisticated update strategies like inserting new documents, merging fields, or replacing existing documents.

Q50. Does MongoDB support foreign key constraints?

Answer: No, MongoDB does not support foreign key constraints due to its flexible, document-based structure.

Q51. What is the role of the profiler in MongoDB?

Answer: The MongoDB profiler collects detailed information about database operations. It’s useful for performance tuning and identifying slow queries.

Conclusion

MongoDB is an important topic to cover while preparing for interviews. It supports CRUD operations, data aggregation, text search, and geospatial queries. The embedded data models and indexing improve query speed and reduce I/O. Replica sets in MongoDB ensure automatic failover and data redundancy. Moreover, it comes with multiple storage engines including the WiredTiger storage engine and an in-memory option, with support for third-party storage engines.

These interview questions are mostly asked in every interview. So prepare yourself with these questions and Keep Learning!

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J
Janvi Kumari

Hi I am Janvi Kumari currently a Associate Insights at Analytics Vidhya, passionate about leveraging data for insights and innovation. Curious, driven, and eager to learn. If you'd like to connect, feel free to reach out to me on LinkedIn

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