How does cloud computing facilitate high-throughput screening in drug discovery? By reducing the need for lab equipment By providing on-demand access to computational resources By increasing the number of manual experiments By eliminating the need for data analysis

By providing on-demand access to computational resources

In molecular modeling, what cloud computing feature is most beneficial? Automated physical lab tests Real-time collaboration tools Access to powerful computational algorithms Increased local storage capacity

Access to powerful computational algorithms

Which cloud service model is most commonly used for drug discovery computational needs? Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Hardware as a Service (HaaS)

Infrastructure as a Service (IaaS)

What is a significant benefit of cloud-based data sharing in drug discovery? Increased data silos Enhanced collaborative research Slower data transfer speeds Limited access to data analytics

Enhanced collaborative research

How can cloud computing reduce costs in drug discovery? By eliminating the need for quality control By reducing the need for physical data centers By increasing the complexity of data analysis By decreasing the number of research collaborations

By reducing the need for physical data centers

Which of the following is a common challenge associated with cloud computing in drug discovery? Difficulty in accessing large datasets Limited scalability of resources Data security and privacy concerns Lack of computational power

Data security and privacy concerns

What role does cloud computing play in virtual drug design? It limits the ability to simulate drug interactions. It provides access to advanced simulation tools and resources. It decreases the speed of experimental results. It prevents real-time data analysis.

It provides access to advanced simulation tools and resources.

Why is the elasticity of cloud services important for drug discovery pipelines? It reduces the need for big data analytics. It allows flexibility in scaling computational resources. It eliminates the need for data visualization tools. It limits access to high-performance computing.

It allows flexibility in scaling computational resources.

Which of the following best describes the role of cloud computing in high-throughput screening (HTS) for drug discovery? Enables real-time data analysis and reduces computational costs Increases the time required for data processing Eliminates the need for data storage Reduces the accuracy of results

Enables real-time data analysis and reduces computational costs

What is a significant advantage of using cloud computing for molecular modeling in drug discovery? Limited access to computational resources Enhanced scalability and flexibility Increased hardware maintenance requirements Reduced data security

Enhanced scalability and flexibility

How does cloud computing facilitate collaborative data sharing in drug discovery pipelines? By restricting access to data between teams By providing centralized databases accessible to multiple stakeholders By requiring physical data transfer between locations By limiting data access to only local researchers

By providing centralized databases accessible to multiple stakeholders

Which cloud service model is most commonly used for conducting simulations and molecular dynamics in drug discovery? Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Network as a Service (NaaS)

Infrastructure as a Service (IaaS)

Which of the following is a benefit of cloud-based machine learning in drug discovery? Increased manual data curation Reduced prediction accuracy Faster processing of large datasets Decreased computational efficiency

Faster processing of large datasets

How does cloud computing impact the cost structure of drug discovery processes? Increases capital expenditure Reduces the need for upfront investments in IT infrastructure Eliminates operational costs Increases variable costs significantly

Reduces the need for upfront investments in IT infrastructure

Which aspect of cloud computing enhances the reproducibility of experiments in drug discovery? Decentralized data processing Standardized computational environments Increased local data storage Reduced data standardization

Standardized computational environments

What is a disadvantage of using REST APIs compared to gRPC for model serving? Lower efficiency for large messages Complex implementation Limited language support High bandwidth requirement

Lower efficiency for large messages

What is the primary purpose of model serving in machine learning? To store data securely To serve models for real-time inference To train models efficiently To visualize data insights

To serve models for real-time inference

What is an advantage of using serverless deployment for model serving? Requires constant server management Automatically scales with demand Limited to small models High latency issues

Automatically scales with demand

Which of the following best describes edge deployment? Hosting models on cloud servers Deploying models on local devices Using models for batch processing Centralized data processing

Deploying models on local devices

In which scenario is gRPC preferred over REST? When using text-based data For high-performance, low-latency applications When no client-server communication is needed For batch data processing

For high-performance, low-latency applications

What was the major limitation of early machine learning models such as the perceptron? Inability to solve non-linear problems High computational cost Lack of data for training Complex implementation

Inability to solve non-linear problems

What is a characteristic feature of the AdaBoost algorithm introduced in the 1990s? Combining weak classifiers to form a strong classifier Minimizing the error in unsupervised learning Utilizing genetic mutations to optimize predictions Implementing a single-layer neural network

Combining weak classifiers to form a strong classifier

Which conference, initiated in 1987, is one of the leading annual gatherings for machine learning research? Neural Information Processing Systems (NeurIPS) International Conference on Machine Learning (ICML) Conference on Computer Vision and Pattern Recognition (CVPR) Association for Computational Linguistics (ACL)

Neural Information Processing Systems (NeurIPS)

Why did the 'AI Winter' occur in the 1970s and 1980s? Failed predictions and lack of computing power Excessive funding leading to oversaturation Over-dependence on expert systems Rapid advancements outpacing understanding

Failed predictions and lack of computing power

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