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Model Deployment & MLOps

Topic in AI / Machine Learning & Data Analytics

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MLOps applies DevOps discipline to machine learning, automating how models are trained, versioned, deployed, monitored and retrained in production. Questions cover experiment tracking with tools such as MLflow, model registries, data and model versioning, and CI/CD pipelines for ML. Deployment items compare batch and real-time inference, REST model serving, Docker containers and Kubernetes. Monitoring questions distinguish data drift, a change in input distributions, from concept drift, a change in the input-output relationship, and explain training-serving skew. Safe rollout strategies such as A/B tests, canary releases and shadow mode deployment are also common.

Below are 30 practice questions from a pool of 210 Model Deployment & MLOps MCQs, one of 17 topics in AI / Machine Learning & Data Analytics. Each shows the correct answer with an explanation; when you are ready, take a timed quiz to test recall under exam conditions.

Practice Questions

Each question below shows the correct answer with a full explanation. Use these to build conceptual understanding before attempting a timed quiz.

Model Deployment & MLOpsEasy

Q1. What is model monitoring?

  1. A.Collecting and gathering new raw data from external sources
  2. B.Training new models from scratch on fresh training data
  3. C.Building automated data preprocessing pipeline workflows
  4. D.Tracking model performance and behavior in production over time✓ Correct

Explanation

Model monitoring continuously tracks deployed model performance, data quality, and system metrics to detect degradation or issues.

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Model Deployment & MLOpsEasy

Q2. What is model versioning?

  1. A.Tracking different versions of a model over time✓ Correct
  2. B.Deleting old models from the storage system
  3. C.Reducing model size through weight pruning
  4. D.Training models to converge significantly faster

Explanation

Model versioning tracks changes to models, their configurations, and artifacts over time, enabling reproducibility and rollback.

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Model Deployment & MLOpsEasy

Q3. What is an API in the context of model deployment?

  1. A.A gradient-based training algorithm for optimizing model weights
  2. B.An interactive visualization tool for exploring model results
  3. C.An interface allowing applications to request predictions from the model✓ Correct
  4. D.A relational database for storing structured training data

Explanation

An API (Application Programming Interface) provides a way for other applications to send data and receive predictions from a deployed ML model.

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Model Deployment & MLOpsEasy

Q4. What is a Docker container?

  1. A.A physical rack-mounted server housed in a data center
  2. B.A standalone relational database for transactional queries
  3. C.A multi-layer deep neural network model architecture
  4. D.A lightweight portable package containing everything to run an application✓ Correct

Explanation

A Docker container packages an application with its dependencies into a standardized unit, ensuring consistent execution across different environments.

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Model Deployment & MLOpsEasy

Q5. What is CI/CD in MLOps?

  1. A.An interactive data visualization and dashboard charting tool
  2. B.A standalone relational database management query system
  3. C.A general-purpose compiled programming language for building apps
  4. D.Continuous Integration and Continuous Deployment for automating ML pipelines✓ Correct

Explanation

CI/CD automates the process of integrating code changes, testing, and deploying ML models, enabling rapid and reliable iteration.

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Model Deployment & MLOpsEasy

Q6. What is a REST API?

  1. A.A gradient-based model training optimization method
  2. B.A type of relational database storage engine format
  3. C.An API architecture using HTTP methods for communication✓ Correct
  4. D.A general-purpose compiled programming language

Explanation

A REST (Representational State Transfer) API uses HTTP methods (GET, POST, etc.) for communication, commonly used to serve ML model predictions.

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Model Deployment & MLOpsEasy

Q7. What is model deployment?

  1. A.Collecting and gathering raw data from sources
  2. B.Making a trained model available for use in production✓ Correct
  3. C.Training a model on labeled training data samples
  4. D.Preprocessing and cleaning the raw input data

Explanation

Model deployment is the process of making a trained ML model available to serve predictions in a production environment.

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Model Deployment & MLOpsEasy

Q8. What is MLOps?

  1. A.Only the model training phase of the ML lifecycle
  2. B.Only the data visualization phase of the lifecycle
  3. C.Practices for deploying and maintaining ML models in production✓ Correct
  4. D.Only the data collection phase of the ML lifecycle

Explanation

MLOps (Machine Learning Operations) combines ML, DevOps, and data engineering practices to deploy, monitor, and maintain ML models in production reliably.

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Model Deployment & MLOpsEasy

Q9. What is a model artifact?

  1. A.The saved output of training including model weights and configuration✓ Correct
  2. B.Only the source code used to build and train the model
  3. C.Only the runtime logs generated during model training
  4. D.Only the raw training data used during model training

Explanation

A model artifact is the serialized output of training, typically including model weights, architecture, hyperparameters, and metadata needed for deployment.

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Model Deployment & MLOpsMedium

Q10. What is canary deployment?

  1. A.Rolling back a deployed model to an earlier stable version
  2. B.Gradually rolling out a new model to a small percentage of users first✓ Correct
  3. C.Deploying the new model to all users immediately without rollout
  4. D.Training a brand new model from scratch on latest data

Explanation

Canary deployment routes a small percentage of traffic to the new model version, monitoring for issues before gradually increasing to full deployment.

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Model Deployment & MLOpsMedium

Q11. What is data drift?

  1. A.Changes in the statistical distribution of input data over time✓ Correct
  2. B.Changes in the source code of the training pipeline
  3. C.Hardware failures in the production serving cluster
  4. D.Changes in the internal model architecture configuration

Explanation

Data drift occurs when the statistical properties of model input data change compared to training data, potentially degrading model performance.

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Model Deployment & MLOpsMedium

Q12. What is the purpose of a model registry?

  1. A.Storing and managing raw training data in a data lake
  2. B.Centrally managing model versions metadata and deployment status✓ Correct
  3. C.Visualizing model performance results on a dashboard
  4. D.Writing and version-controlling source code for models

Explanation

A model registry tracks model versions, their metadata, approval status, and deployment history, providing governance and collaboration for ML teams.

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Model Deployment & MLOpsMedium

Q13. What is model drift?

  1. A.Model training getting significantly faster with each iteration
  2. B.Degradation in model performance over time due to changes in data✓ Correct
  3. C.Model file size increasing automatically without configuration
  4. D.Model accuracy consistently increasing without retraining

Explanation

Model drift occurs when the statistical properties of the target variable or input data change over time, causing the deployed model's performance to degrade.

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Model Deployment & MLOpsMedium

Q14. What is A/B testing in model deployment?

  1. A.Comparing two model versions by serving them to different user groups✓ Correct
  2. B.Running computations on two GPUs for faster processing speed
  3. C.Training two different models simultaneously on the same data
  4. D.Using two separate datasets for training and evaluation only

Explanation

A/B testing deploys two model versions simultaneously to different user segments, comparing their performance to determine which is better.

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Model Deployment & MLOpsEasy

Q15. What does reproducibility mean in ML?

  1. A.Using less training data to save storage and costs
  2. B.Running trained models faster by using better hardware
  3. C.The ability to recreate the same results with the same data and code✓ Correct
  4. D.Making models larger by adding more network layers

Explanation

Reproducibility ensures that the same data, code, and configurations produce identical results, essential for debugging and compliance in ML systems.

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Model Deployment & MLOpsMedium

Q16. What is a feature store?

  1. A.A model registry for tracking model versions and status
  2. B.A code repository for managing source version control
  3. C.A centralized repository for storing managing and serving ML features✓ Correct
  4. D.A data warehouse for storing raw analytical data records

Explanation

A feature store provides a centralized system for defining, storing, and serving features consistently across training and inference, ensuring feature consistency.

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Model Deployment & MLOpsMedium

Q17. What is model serialization?

  1. A.Deploying a model to a production serving endpoint
  2. B.Saving a trained model to a file format that can be loaded later✓ Correct
  3. C.Training a model on a labeled dataset from scratch
  4. D.Evaluating a model's accuracy on a held-out test set

Explanation

Model serialization converts a trained model into a storable format (e.g., pickle, ONNX, SavedModel) for persistence and deployment.

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Model Deployment & MLOpsHard

Q18. What is concept drift vs data drift?

  1. A.Data drift only affects the target output variable distribution
  2. B.Concept drift only affects the distribution of input features alone
  3. C.Concept drift changes input-target relationship; data drift changes input distribution✓ Correct
  4. D.They are completely identical phenomena with no meaningful differences

Explanation

Data drift is a change in input data distribution. Concept drift is a change in the relationship between inputs and the target variable. Both degrade model performance but require different detection strategies.

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Model Deployment & MLOpsHard

Q19. What is the purpose of experiment tracking tools like MLflow?

  1. A.Recording parameters metrics code and artifacts for each experiment✓ Correct
  2. B.Only visualizing final model results on a dashboard
  3. C.Only storing the raw training data on a file system
  4. D.Only deploying models to a production serving endpoint

Explanation

Experiment tracking tools log hyperparameters, metrics, code versions, and model artifacts for each experiment, enabling comparison, reproducibility, and collaboration.

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Model Deployment & MLOpsHard

Q20. What is model explainability in production?

  1. A.Storing more raw training data for future retraining sessions
  2. B.Providing interpretable explanations for why a model makes specific predictions✓ Correct
  3. C.Making the model inference significantly faster and more efficient
  4. D.Reducing the overall size and memory footprint of the model

Explanation

Model explainability provides interpretable reasons for predictions, critical for debugging, compliance (GDPR), trust, and responsible AI deployment.

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Model Deployment & MLOpsMedium

Q21. What is a ML pipeline?

  1. A.A database query for extracting records from tables
  2. B.An automated sequence of steps from data processing to model deployment✓ Correct
  3. C.An interactive data visualization charting dashboard
  4. D.A single isolated model training step without automation

Explanation

An ML pipeline automates the end-to-end workflow including data ingestion, preprocessing, training, evaluation, and deployment in a reproducible sequence.

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Model Deployment & MLOpsMedium

Q22. What is the ONNX format?

  1. A.A markup text file format for web page content
  2. B.An open format for ML models enabling interoperability between frameworks✓ Correct
  3. C.A proprietary relational database file storage format
  4. D.A compressed video streaming container file format

Explanation

ONNX (Open Neural Network Exchange) is an open format for ML models, allowing models to be transferred between frameworks like PyTorch and TensorFlow.

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Model Deployment & MLOpsHard

Q23. What is the concept of shadow deployment?

  1. A.Running a new model in parallel with production without serving its predictions✓ Correct
  2. B.Deploying the model at night during off-peak hours only
  3. C.A/B testing between two model versions with live traffic
  4. D.Canary deployment with a small percentage of live users

Explanation

Shadow deployment runs the new model alongside the production model, logging predictions for comparison without affecting users, enabling risk-free evaluation with real traffic.

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Model Deployment & MLOpsHard

Q24. What is the blue-green deployment strategy?

  1. A.A data visualization technique using a blue-green color palette
  2. B.Running models on GPUs with blue and green LED indicator lights
  3. C.A model training strategy using two alternating learning rates
  4. D.Maintaining two identical production environments and switching traffic between them✓ Correct

Explanation

Blue-green deployment maintains two production environments. Traffic is switched from the current (blue) to the new (green) version, enabling instant rollback by switching back.

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Model Deployment & MLOpsMedium

Q25. What is the role of Kubernetes in MLOps?

  1. A.Visualizing model performance on dashboard charts
  2. B.Orchestrating containerized ML services for scaling and management✓ Correct
  3. C.Storing raw data files on a distributed file system
  4. D.Training machine learning models on training data

Explanation

Kubernetes orchestrates Docker containers, providing automatic scaling, load balancing, and management of ML services in production environments.

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Model Deployment & MLOpsHard

Q26. What is GitOps for ML?

  1. A.Using Git as single source of truth for ML infrastructure configs and deployments✓ Correct
  2. B.Only storing the Python source code files in a Git repository
  3. C.A gradient-based training method for optimizing neural networks
  4. D.A type of version control system different from standard Git

Explanation

GitOps extends Git-based workflows to ML operations, using Git repositories as the source of truth for infrastructure definitions, model configurations, and deployment specifications.

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Model Deployment & MLOpsHard

Q27. What is the role of infrastructure as code (IaC) in MLOps?

  1. A.Collecting and gathering raw data from external data sources
  2. B.Writing machine learning algorithm implementations and model code
  3. C.Defining and managing ML infrastructure through code for reproducibility✓ Correct
  4. D.Training machine learning models on labeled training datasets

Explanation

IaC (e.g., Terraform, CloudFormation) manages ML infrastructure through version-controlled code, ensuring reproducible, consistent, and automated environment setup.

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Model Deployment & MLOpsHard

Q28. What is a serving graph in ML deployment?

  1. A.A computation graph used only during the model training process
  2. B.An interactive data visualization chart for exploring model outputs
  3. C.A diagram showing the internal layers and neurons of a network
  4. D.A DAG of preprocessing inference and postprocessing steps during prediction✓ Correct

Explanation

A serving graph defines the directed acyclic graph (DAG) of operations executed during inference, including feature transformation, model prediction, and result postprocessing.

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Model Deployment & MLOpsHard

Q29. What is model compression for deployment?

  1. A.Increasing numerical precision of weights from FP16 to FP64
  2. B.Adding more hidden layers to increase the depth of network
  3. C.Reducing model size and complexity while maintaining acceptable performance✓ Correct
  4. D.Making models significantly larger by adding trainable parameters

Explanation

Model compression techniques (pruning, quantization, knowledge distillation) reduce model size and computation requirements for deployment on resource-constrained devices.

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Model Deployment & MLOpsHard

Q30. What is the challenge of training-serving skew?

  1. A.The training dataset being too small for adequate model learning overall
  2. B.The model training process converging far too slowly to be practical✓ Correct
  3. C.Models becoming too large to fit within the available GPU memory capacity
  4. D.Differences between training and serving environments causing inconsistent predictions

Explanation

Training-serving skew occurs when data processing or feature computation differs between training and serving, causing the model to receive different inputs than it was trained on.

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