MCA DCA72A4 AI IN CLOUD

Description

SESSION	Feb-March 2026
PROGRAM	MASTER OF COMPUTER APPLICATIONS (MCA)
SEMESTER	II / III / IV
COURSE CODE & NAME	DCA72A4 AI IN CLOUD

 

 

 

Set – 1

 

Q.1. Explain the different types of Artificial Intelligence (Reactive Machines, Limited Memory, Theory of Mind, and Self-aware AI). Analyse their capabilities and limitations with suitable examples.

Ans 1.

Types of Artificial Intelligence

Artificial Intelligence Systems are classified as four different types based upon their abilities to think and to understand or think about their environment.

Reactive Machines

Reactive Machines represent the most basic type of AI. These systems process current inputs and create outputs using

 

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Q.2. Give the architecture and working of TensorFlow and PyTorch frameworks. Discuss their associated cloud-based deployment tools and their role in scalable model serving.

Ans 2.

TensorFlow: Architecture and Working

TensorFlow is an open source machine-learning framework created by Google Brain and released publicly in the year 2015. The architecture of the framework is built on computational graphs. Nodes represent mathematical operations and edges represent the multidimensional data arrays referred to tensors moving between each other. In TensorFlow 2.x the eager execution feature is activated by default, allowing processes to test immediately and without having to build an initial static graph which makes development and troubleshooting easier for users. The framework

 

 

 

Q.3. Compare and contrast supervised, unsupervised, and reinforcement learning paradigms. Illustrate each with suitable real-world examples and highlight their key differences.

Ans 3.

Supervised Learning

Supervised learning is the process of training models on data labeled and each example of training is paired with a corresponding accurate output. It learns how to convert the inputs into outputs by decreasing the variance between its forecasts and the actual labels. The two main tasks are classification and regression. two main duties. Image classification systems that identify the presence of a pet or a cat are built on millions of

 

 

Set – 2

 

Q.4. Analyse the role of cloud-based computer vision services in modern applications. How do object detection and image classification techniques improve automation and decision-making?

Ans 4.

Cloud-Based Computer Vision Services

Cloud-based computer vision solutions provide the ability to use ready-to-use AI capabilities to understand visual information using easy API calls. This eliminates the requirement for companies to develop and train their own deep-learning models completely from scratch. Cloud providers that are leading in their field, such as AWS Rekognition,

 

Q.5. What are cloud-based Natural Language Processing (NLP) services? Discuss key NLP tasks such as sentiment analysis, named entity recognition, and machine translation, along with their real-world applications.

Ans 5.

Cloud-Based NLP Services

Cloud-based Natural Language Processing (NLP) services are controlled AI products that help applications to understand, interpret and translate human speech via API-based access to pretrained large models of language. The major cloud platforms, including AWS Comprehend, Google Cloud Natural Language API, Azure Cognitive Services Text Analytics, and OpenAI API

 

 

Q.6. How cloud-based AI pipelines enable efficient development and deployment of machine learning models? Explain each stage of the pipeline with suitable examples.

Ans 6.

Cloud-Based AI Pipelines

Cloud-based AI pipelines are automated end-to-end processes that simplify every step of developing, training reviewing, and deploying model-based learning at a scale. They abstract infrastructure management, ensure reproducibility, enable collaboration among teams, and help speed the transition of raw data into production-ready models. Major cloud providers offer managed