MCQs

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Overall MCQs for Reference

1. What is containerization in the context of machine learning model deployment?
   a. Packaging ML code into a virtual machine
   b. Packaging ML models and dependencies into portable containers
   c. Deploying models without any dependencies
   d. Creating a new operating system for deployment
   Answer: bdev+1​
2. Which of the following tools is most commonly used for containerizing ML models?
   a. Vagrant
   b. VirtualBox
   c. Docker
   d. Hadoop
   Answer: cstudocu+1​
3. What is a container image?
   a. A type of virtual machine
   b. A build script for an ML model
   c. A lightweight, executable package of software including code and dependencies
   d. A data visualization library
   Answer: cstudocu+1​
4. Which file typically describes build instructions for a Docker image?
   a. Manifest.json
   b. Dockerfile
   c. Makefile
   d. requirements.txt
   Answer: bashutoshtripathi+1​
5. What is the primary purpose of containerizing an ML model?
   a. To reduce training time
   b. To improve development speed only
   c. To ensure consistency and portability across environments
   d. To improve data accuracy
   Answer: cgeeksforgeeks+2​
6. Which command builds a Docker image from a Dockerfile?
   a. docker run
   b. docker build
   c. docker execute
   d. docker create
   Answer: bstudocu​
7. What does “docker run” do?
   a. Creates a Dockerfile
   b. Fetches an online image
   c. Runs a container from an image
   d. Deletes a container
   Answer: cashutoshtripathi+1​
8. What is container orchestration?
   a. The process of visualizing containers
   b. Automated deployment and management of containers at scale
   c. Building images from source code only
   d. Training models in batches
   Answer: bgeeksforgeeks+1​
9. Which platform is widely used for orchestrating containerized ML workloads?
   a. Apache Spark
   b. Kubernetes
   c. Hadoop YARN
   d. Ansible
   Answer: bsanfoundry+1​
10. What is one major advantage of using containers over virtual machines for ML deployment?
    a. More resource-intensive
    b. Slower start-up time
    c. Less resource overhead and faster start-up
    d. Requires new hardware
    Answer: cdev+1​
11. Which of the following best describes a Docker container?
    a. A complete operating system simulator
    b. An isolated, lightweight software package with application code and dependencies
    c. A static web server
    d. An external storage device
    Answer: bdev+1​
12. Which command lists currently running Docker containers?
    a. docker images
    b. docker ps
    c. docker run
    d. docker build
    Answer: bstudocu​
13. What is a benefit of deploying ML models in containers regarding dependency management?
    a. Dependencies need to be installed on the host manually
    b. Dependencies are bundled, minimizing version conflicts
    c. Dependencies are not allowed
    d. Dependencies are ignored during runtime
    Answer: bashutoshtripathi+1​
14. Which of the following is NOT a benefit of containerization?
    a. Improved scalability
    b. Reduced resource overhead
    c. Increased isolation
    d. Slower deployment
    Answer: ddev+1​
15. What is Kubeflow used for?
    a. Training neural networks only
    b. Automating classic ETL pipelines
    c. Orchestrating and deploying ML workflows on Kubernetes
    d. Creating HTML dashboards
    Answer: cgeeksforgeeks​
16. What is the main purpose of exposing a port in a containerized ML application?
    a. To increase model accuracy
    b. To allow external access to the containerized service
    c. To limit container memory
    d. To monitor CPU usage
    Answer: bashutoshtripathi+1​
17. Which statement about Docker Compose is true for ML model deployment?
    a. It manages multiple containers and their configurations as a single service
    b. It is only used for data storage
    c. It creates VMs on demand
    d. It increases GPU utilization
    Answer: aashutoshtripathi+1​
18. Which of these is an example of an ML model-serving framework that supports containerization?
    a. TensorFlow Serving
    b. NumPy
    c. Pandas
    d. Matplotlib
    Answer: ageeksforgeeks​
19. Why is “versioning” important when containerizing ML models?
    a. Ensures compatibility between OS and hardware
    b. Helps in tracking and rolling back to previous builds or models
    c. Restricts deployment to Linux OS only
    d. Reduces inference latency
    Answer: bgeeksforgeeks​
20. Which cloud service supports direct deployment of Docker containerized ML models?
    a. AWS Lambda
    b. Google Colab
    c. Jupyter Notebook
    d. MS Word
    Answer: apragnakalp​
21. What is typically bundled in a container image for ML deployment?
    a. Model artifact, code, dependencies, and configuration files
    b. Only raw data
    c. Only command-line tools
    d. Hardware drivers
    Answer: adev+1​
22. How do containers facilitate model reproducibility?
    a. By ignoring OS differences and packaging code + dependencies
    b. By requiring on-premise deployment always
    c. By using only raw binaries
    d. By connecting to external APIs
    Answer: adev+1​
23. What does “container registry” refer to?
    a. List of installed models
    b. Online repository for storing and sharing container images
    c. Default folder for datasets
    d. Visualization module
    Answer: bashutoshtripathi+1​
24. Which technology enables horizontal scaling of ML inference via containers?
    a. USB hubs
    b. Container orchestrators (e.g., Kubernetes)
    c. Excel macros
    d. Device drivers
    Answer: bsanfoundry+1​
25. What is the recommended way to pass configuration variables into a container?
    a. By editing container internals
    b. By using environment variables
    c. Hard-coded into source code
    d. Via USB stick
    Answer: bashutoshtripathi+1​
26. Which of these is true about multi-stage builds in Docker for ML projects?
    a. Always slower
    b. Allows separation of build and runtime, reducing image size
    c. Is not recommended
    d. Creates multiple containers per build
    Answer: bdev+1​
27. Why might you use GPU support in your ML container?
    a. For rendering HTML
    b. For faster model inference/training
    c. For file compression
    d. For data entry automation
    Answer: bashutoshtripathi​
28. What is TensorFlow Serving frequently used for?
    a. Feature engineering
    b. Serving machine learning models in production using container deployments
    c. Dataset creation
    d. Plotting results
    Answer: bgeeksforgeeks​
29. What does the ‘CMD’ instruction in a Dockerfile specify?
    a. The working directory
    b. The default command to run when container starts
    c. Image size limit
    d. Model input schema
    Answer: bdev+1​
30. What is an example of a prediction service created by containerizing an ML model?
    a. Flask API wrapped in a Docker container
    b. Excel worksheet formula
    c. Java applet
    d. LaTeX document
    Answer: aashutoshtripathi+1​
31. Why is containerization considered essential for CI/CD pipelines in ML?
    a. It slows down deployment
    b. It ensures consistent, automated testing and delivery across all environments
    c. Requires more manual steps
    d. It works only offline
    Answer: bgeeksforgeeks+1​
32. Which component in Kubernetes is mainly responsible for handling container deployment?
    a. kube-controller-manager
    b. kube-apiserver
    c. kubelet
    d. kube-scheduler
    Answer: asanfoundry​
33. For ML model inference, what is the advantage of exposing a REST API inside a container?
    a. Allows model training only
    b. Enables programmatic access to predictions remotely
    c. Blocks external requests
    d. Increases server memory usage
    Answer: bdev+1​
34. What is the primary function of an entrypoint script in a containerized ML application?
    a. Sets the model hyperparameter
    b. Commands that are run by default when a container starts
    c. Specifies the OS version
    d. Installs external hardware
    Answer: bashutoshtripathi+1​
35. Which of the following can be versioned in a container registry?
    a. Docker images of ML models
    b. CSV files only
    c. Browser cookies
    d. Only hyperparameter files
    Answer: adev+1​
36. What is the main reason to use ‘docker-compose.yml’ in ML model deployment?
    a. To manage multi-container applications and networks
    b. To compile Java files
    c. To encrypt datasets
    d. To create new languages
    Answer: aashutoshtripathi+1​
37. Which Kubernetes resource is typically used to expose a containerized ML model to external traffic?
    a. Secret
    b. Service
    c. Pod
    d. Namespace
    Answer: bsanfoundry​
38. What is the use of the “requirements.txt” inside a containerized ML project?
    a. Lists web server URLs
    b. Specifies Python dependencies for the project
    c. Contains the data files
    d. Manages container networking settings
    Answer: bdev+1​
39. Which storage solution is commonly used to persist model artifacts outside containers?
    a. Local host directory mount
    b. Docker image tags
    c. Swap file partition
    d. DNS lookup cache
    Answer: aashutoshtripathi+1​
40. What is NOT encapsulated in a containerized ML application?
    a. Model binary
    b. Application configuration
    c. Host OS kernel
    d. Library dependencies
    Answer: cdev+1​
41. What does container scaling mean for ML deployments?
    a. Increasing CPU in the host machine
    b. Running multiple instances of containerized services to handle more load
    c. Adding more logs
    d. Renaming containers
    Answer: bsanfoundry+1​
42. Which of the following is a best practice for securing ML containers?
    a. Embedding secrets in the code
    b. Using secret management and limiting permissions
    c. Allowing open ports only
    d. Using root user always
    Answer: bgeeksforgeeks+1​
43. Which Docker command is used to download an image from a registry?
    a. docker save
    b. docker pull
    c. docker extract
    d. docker upload
    Answer: bstudocu​
44. What is “container drift” in the context of ML deployment?
    a. Container appearance changes
    b. Difference between running container state and original image or configuration
    c. Slower prediction time
    d. Increased model accuracy
    Answer: bashutoshtripathi+1​
45. Which tool can help automate retraining and redeploying containerized ML models?
    a. Bamboo
    b. MLflow
    c. Adobe InDesign
    d. REST API only
    Answer: bgeeksforgeeks​
46. What is the purpose of health checks in containerized ML applications?
    a. Increase training speed
    b. Automatically restart containers not responding or unhealthy
    c. Generate more logs
    d. Reduce memory usage
    Answer: bsanfoundry+1​
47. What would you use to store large datasets for access by containerized ML models?
    a. RAM
    b. External file storage, such as S3 buckets
    c. HTML files
    d. IP addresses
    Answer: bgeeksforgeeks​
48. What does “stateless” mean for a containerized ML model service?
    a. Retains data between requests
    b. Does not store data about previous requests (no session information)
    c. Always retrains model on use
    d. Needs a physical server
    Answer: bgeeksforgeeks+1​
49. Which cloud-native service supports serverless deployment of containerized ML workloads?
    a. AWS Lambda with Docker images
    b. Microsoft PowerPoint
    c. Excel Macros
    d. Pure Java server
    Answer: apragnakalp​
50. What is a primary reason for using containers in distributed ML model deployment scenarios?
    a. Consistency and portability of workloads across infrastructures
    b. Only for increasing dataset size
    c. Replacing GPUs
    d. Minimizing dataset features
    Answer: ageeksforgeeks+2​

1. https://dev.to/pavanbelagatti/a-step-by-step-guide-to-containerizing-and-deploying-machine-learning-models-with-docker-21al
2. https://www.geeksforgeeks.org/machine-learning/machine-learning-deployment/
3. https://www.studocu.com/in/document/savitribai-phule-pune-university/computer-engineering/container-mcq/92253063
4. https://ashutoshtripathi.com/2023/03/25/machine-learning-model-deployment-using-docker-container/
5. https://www.sanfoundry.com/kubernetes-mcq-multiple-choice-questions/
6. https://www.pragnakalp.com/how-to-deploy-machine-learning-models-on-aws-lambda-using-docker/
7. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
8. https://www.interviewbit.com/machine-learning-mcq/
9. https://futureskillsacademy.com/blog/top-machine-learning-mcqs-with-answers/
10. https://www.placementpreparation.io/mcq/machine-learning/
11. https://www.youtube.com/watch?v=FugpqOEPuBg
12. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
13. https://www.igmguru.com/blog/machine-learning-mcqs
14. https://www.geeksforgeeks.org/quizzes/machine-learning-quiz-questions-and-answers/
15. https://interviewkickstart.com/blogs/articles/machine-learning-mcqs-answers
16. https://www.javacodemonk.com/machine-learning-based-multiple-choice-questions-626ca098
17. https://www.scribd.com/document/526386199/Machine-Learning-Solved-Mcqs-set-1-McqMate-com
18. https://diptisurvase.wordpress.com/wp-content/uploads/2022/09/ml-mcqs.pdf
19. https://www.placementpreparation.io/mcq/azure/page/5/
20. https://letsfindcourse.com/data-science/machine-learning-mcq-questions-and-answers