MCSL-229 IGNOU Solved Assignment 2026-27

MCSL-229 Solved Assignment: Cloud and Data Science Lab Official IGNOU TMA Guide

This document provides 100% verified solutions for the Tutor Marked Assignment (TMA) of MCSL-229 Cloud and Data Science Lab, aligned with IGNOU’s MCA-New curriculum (School of Computer and Information Sciences). Solutions are structured to meet the mandatory 30% course weightage and comply with word limits (500/250/100 words) for each question. All answers incorporate practical implementations, theoretical justifications, and code snippets where applicable.

Syllabus Blocks Covered (Official IGNOU Curriculum)

• Unit 1: Cloud Computing Fundamentals and Virtualization
  • Practical: Deploying a virtual machine on AWS/Azure/GCP with step-by-step screenshots and CLI commands.
  • Theoretical: Comparative analysis of IaaS, PaaS, and SaaS models with real-world use cases (e.g., scalability in startups vs. enterprises).
  • FAQ: How does elastic load balancing differ from auto-scaling in cloud environments? Answer includes code snippets for configuring both in AWS.
• Unit 2: Data Science Tools and Big Data Technologies
  • Practical: Writing a Python script using Pandas to preprocess a dataset (e.g., cleaning missing values, encoding categorical variables) with output visualizations.
  • Theoretical: Explanation of distributed computing frameworks (Hadoop, Spark) with a focus on RDD vs. DataFrame operations in Spark.
  • FAQ: What are the key differences between NoSQL databases (MongoDB, Cassandra) and traditional SQL databases? Answer highlights schema flexibility, query performance, and scalability trade-offs.
• Unit 3: Cloud-Based Data Analytics and Machine Learning
  • Practical: Implementing a simple linear regression model in Python (using Scikit-learn) deployed on Google Cloud AI Platform with inference API calls.
  • Theoretical: Discussion on cloud-native ML tools (TensorFlow Extended, Kubeflow) and their integration with Kubernetes for scalable training.
• Unit 4: Security and Compliance in Cloud Data Science
  • Practical: Configuring IAM roles and encryption (AES-256) for a data lake stored in AWS S3 with policy templates.
  • Theoretical: Analysis of GDPR vs. HIPAA compliance requirements for cloud-stored sensitive data, with a focus on anonymization techniques.
• Unit 5: Case Studies in Cloud and Data Science
  • Practical: Step-by-step breakdown of a real-world case study (e.g., Netflix’s recommendation system) using cloud resources (AWS Lambda, DynamoDB).
  • Theoretical: Critique of ethical considerations in data science (bias, transparency) with examples from cloud-based AI deployments.

Key Submission Guidelines

• Word Limits: Strict adherence to 500 words for theoretical questions, 250 words for short answers, and 100 words for coding snippets.
• Formatting: Use 12pt Times New Roman font, 1.5 line spacing, and numbered questions as per the TMA format sheet.
• Deadlines: Submit within 31 days of course enrollment (check IGNOU’s official submission portal for session-specific deadlines).
• Plagiarism Policy: All solutions are cross-verified with Turnitin; originality report included in the digital PDF.

Dynamic FAQs for Students

• Q: Can I use any cloud provider (AWS/Azure/GCP) for practical questions?

  Yes. However, prefer AWS for consistency, as it is the most commonly referenced in IGNOU’s official lab manual. Include screenshots of your cloud console for full marks.

• Q: Are Python libraries like NumPy and Pandas allowed for data preprocessing?

  Absolutely. All standard open-source libraries (e.g., Scikit-learn, TensorFlow) are permitted. Cite the library versions used in your assignment.

Digital PDF Features

• Searchable PDF with bookmarked sections (Units 1–5).
• Included: Sample TMA format sheet, plagiarism report, and step-by-step submission guide.
• Compatibility: Works on Windows/macOS/Linux with Adobe Acrobat Reader.