MCQs

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Overall MCQs for Reference

Which deployment model allows multiple organizations to share cloud infrastructure for a common purpose?
a. Public Cloud
b. Private Cloud
c. Hybrid Cloud
d. Community Cloud
Answer: d. Community Cloudamnic+1
In machine learning cloud deployments, which model provides the highest level of data privacy?
a. Public Cloud
b. Private Cloud
c. Hybrid Cloud
d. Community Cloud
Answer: b. Private Cloudk21academy+1
What is a key advantage of deploying machine learning models in a public cloud?
a. Unlimited customization
b. Fixed cost
c. Scalability and accessibility
d. No security concerns
Answer: c. Scalability and accessibilityamnic+1
Which cloud deployment allows seamless integration of on-premises infrastructure with cloud services?
a. Public Cloud
b. Private Cloud
c. Hybrid Cloud
d. Community Cloud
Answer: c. Hybrid Cloudk21academy+1
What is a primary security risk associated with public cloud deployments in machine learning?
a. Lack of scalability
b. Vendor lock-in
c. Shared infrastructure
d. Limited data storage
Answer: c. Shared infrastructureamnic+1
Which cloud provider is commonly used for cloud-based machine learning deployments?
a. Dropbox
b. Amazon Web Services (AWS)
c. Facebook
d. GitHub
Answer: b. Amazon Web Services (AWS)rootstack+1
In cloud deployments for machine learning, what feature does AWS SageMaker provide?
a. Data visualization tools
b. Model training, deployment, and monitoring
c. Hardware design tools
d. Mobile app development
Answer: b. Model training, deployment, and monitoringrootstack
What is one limitation of deploying machine learning models in private clouds?
a. Reduced security
b. High initial setup cost
c. Low customization
d. Limited data privacy
Answer: b. High initial setup costk21academy+1
Which deployment model combines public and private clouds for workload distribution?
a. Public Cloud
b. Private Cloud
c. Hybrid Cloud
d. Community Cloud
Answer: c. Hybrid Cloudamnic+1
What is the primary benefit of deploying ML models as REST APIs in the cloud?
a. Reduced accessibility
b. Easy integration with applications
c. Lower model performance
d. Restricted scalability
Answer: b. Easy integration with applicationslinkedin
Before deploying a machine learning model on the cloud, what should be set up first?
a. Final report
b. Development environment
c. User interface
d. Marketing plan
Answer: b. Development environmentlinkedin+1
Which cloud deployment model is ideal for organizations with strict compliance requirements?
a. Public Cloud
b. Community Cloud
c. Private Cloud
d. Hybrid Cloud
Answer: c. Private Cloudk21academy+1
What is a common tool for storing large datasets in the cloud for ML?
a. Azure DevOps
b. Google Cloud Storage
c. Google Docs
d. Visual Studio
Answer: b. Google Cloud Storagerootstack
Which factor is NOT typically considered when selecting a cloud platform for ML deployment?
a. Costs
b. Integration
c. Weather
d. Supported frameworks
Answer: c. Weatherlinkedin+1
What is the function of Google Cloud AI Platform?
a. Data storage only
b. Training, deploying, and managing ML models
c. User authentication
d. Email hosting
Answer: b. Training, deploying, and managing ML modelsrootstack
What type of cloud deployment shared between organizations with similar interests is called?
a. Hybrid Cloud
b. Private Cloud
c. Community Cloud
d. Public Cloud
Answer: c. Community Cloudk21academy
Which is a benefit of deploying machine learning models in the cloud?
a. Limited scalability
b. Flexible computing resources
c. Complex local infrastructure
d. Fixed resource allocation
Answer: b. Flexible computing resourcesamnic+1
What strategy is often used to expose ML models in cloud deployments for external access?
a. As a desktop application
b. As a REST API
c. Via Email
d. As an Excel plugin
Answer: b. As a REST APIlinkedin
Which cloud deployment model would best suit a university collaborating on research projects?
a. Public Cloud
b. Community Cloud
c. Private Cloud
d. Hybrid Cloud
Answer: b. Community Cloudamnic+1
What is vendor lock-in in the context of cloud deployments?
a. Easy migration
b. Difficulty switching providers
c. Increased data privacy
d. Reduced workload
Answer: b. Difficulty switching providersk21academy+1
Which service is directly associated with deploying and scaling machine learning models on AWS?
a. AWS Lambda
b. AWS SageMaker
c. AWS Cloud9
d. AWS Amplify
Answer: b. AWS SageMakerrootstack+1
What is the role of cloud storage in ML cloud deployments?
a. Reducing performance
b. Efficient, scalable storage of large datasets
c. Restricting data access
d. Deleting user data
Answer: b. Efficient, scalable storage of large datasetsrootstack+1
What is a critical step before deploying data to a machine learning model in the cloud?
a. Data cleaning and preprocessing
b. Building the web interface
c. Ignoring outliers
d. Data encryption only
Answer: a. Data cleaning and preprocessingrootstack
Which Google service is used to store machine learning datasets in the cloud?
a. Google Drive
b. Google Cloud Storage
c. Google Sheets
d. Google Sites
Answer: b. Google Cloud Storagerootstack
A hybrid cloud deployment provides which of the following features?
a. Data isolation only
b. Combination of public and private cloud advantages
c. Only on-premise management
d. Fixed hardware allocation
Answer: b. Combination of public and private cloud advantagesamnic+1
When deploying ML models to the cloud, what is typically automated for efficiency?
a. Hardware upgrades
b. Infrastructure provisioning
c. UI/UX design
d. Resource deletion
Answer: b. Infrastructure provisioninglinkedin+1
Which of the following is NOT an advantage of cloud ML deployments?
a. Enhanced collaboration
b. Global access
c. Easy scaling
d. Fixed computing capacity
Answer: d. Fixed computing capacityk21academy+1
The deployment model where the infrastructure is owned by a third party and shared with multiple organizations is:
a. Private Cloud
b. Community Cloud
c. Public Cloud
d. Hybrid Cloud
Answer: c. Public Cloudamnic+1
Which model provides the most customization for ML cloud deployments?
a. Public Cloud
b. Private Cloud
c. Hybrid Cloud
d. Community Cloud
Answer: b. Private Cloudk21academy+1
Before managing machine learning artifacts, what must be established in the cloud?
a. Security keys
b. Development and storage environment
c. Only a worksheet
d. AI ethics board
Answer: b. Development and storage environmentlinkedin+1
Which platform supports notebook-based ML development for cloud deployment?
a. AWS SageMaker
b. Yahoo Mail
c. WhatsApp
d. Zoom
Answer: a. AWS SageMakerrootstack
Which is a challenge of machine learning cloud deployments?
a. Unlimited security
b. Vendor Lock-in
c. No access control
d. Fixed resource usage
Answer: b. Vendor Lock-inamnic+1
What is the main benefit of using cloud computing for machine learning model deployments?
a. On-premises maintenance
b. Reduced flexibility
c. Cost-efficiency and scalability
d. Permanent hardware purchase
Answer: c. Cost-efficiency and scalabilityk21academy+1
Which method enables real-time predictions from cloud-deployed ML models?
a. Emails
b. RESTful APIs
c. Batch updates
d. Manual polling
Answer: b. RESTful APIslinkedin
Which cloud deployment model is optimal for fluctuating resource demands in ML?
a. Hybrid Cloud
b. Public Cloud
c. Private Cloud
d. Community Cloud
Answer: b. Public Cloudamnic+1
What is a common practice to ensure data integrity in cloud ML deployments?
a. Ignoring backups
b. Automatic backup and recovery
c. Manual copy only
d. Irregular dataset checks
Answer: b. Automatic backup and recoveryrootstack+1
Which deployment model allows organizations to maintain sensitive workloads on-premise while using cloud for non-sensitive tasks?
a. Hybrid Cloud
b. Private Cloud
c. Community Cloud
d. Public Cloud
Answer: a. Hybrid Cloudk21academy+1
What type of cloud model is best for small businesses looking for cost-effective ML deployment with minimal IT management?
a. Private Cloud
b. Community Cloud
c. Public Cloud
d. Hybrid Cloud
Answer: c. Public Cloudamnic+1
What does “cloud-native” in ML deployment refer to?
a. Running models on physical servers
b. Building models to leverage cloud scalability and services
c. Ignoring cloud benefits
d. Minimal use of automation
Answer: b. Building models to leverage cloud scalability and servicescloudoptimo+1
Which is an example of a cloud-native ML service?
a. AWS SageMaker
b. Microsoft Word
c. Adobe Photoshop
d. Linux Kernel
Answer: a. AWS SageMakerrootstack+1
Which is NOT a core characteristic of cloud deployment models?
a. Ownership
b. Security
c. Location
d. Weather conditions
Answer: d. Weather conditionsgeeksforgeeks+1
What enables secure access to machine learning models deployed in the cloud?
a. Public URLs without authentication
b. API authentication and network security
c. Open SSH for all
d. Physical access only
Answer: b. API authentication and network securitylinkedin+1
Which cloud deployment model is least likely to introduce compliance risks for sensitive data?
a. Public Cloud
b. Private Cloud
c. Hybrid Cloud
d. Community Cloud
Answer: b. Private Cloudk21academy+1
What is a primary computational benefit of deploying ML models in the cloud?
a. Restricted performance
b. Elastic compute resources
c. Unchangeable processing speed
d. Fixed quotas
Answer: b. Elastic compute resourcesamnic+1
Which challenge is common when moving ML workloads between cloud providers?
a. Vendor lock-in
b. Easy migration
c. Automatic compatibility
d. Universal data formats
Answer: a. Vendor lock-ink21academy+1
Which service is often used for workflow automation in ML model cloud deployment?
a. AWS Lambda
b. Windows Explorer
c. Steam
d. Eclipse
Answer: a. AWS Lambdarootstack+1
What is typically the first step in machine learning cloud deployment?
a. Reporting results
b. Selecting the cloud platform
c. Hiring a team
d. Sending emails
Answer: b. Selecting the cloud platformlinkedin+1
Which one is NOT a standard cloud deployment model?
a. Public Cloud
b. Private Cloud
c. Community Cloud
d. Universal Cloud
Answer: d. Universal Cloudgeeksforgeeks+1
What helps organizations manage multiple machine learning deployments across public and private clouds?
a. Hybrid Cloud Management Tools
b. Email notifications
c. Data entry jobs
d. Web browsers
Answer: a. Hybrid Cloud Management Toolsamnic+1
Which cloud deployment approach is best suited for cross-industry collaborations in ML initiatives?
a. Community Cloud
b. Private Cloud
c. Public Cloud
d. Hybrid Cloud
Answer: a. Community Cloudk21academy+1

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