Here’s the updated version of the answers with points adjusted according to the marks:

1. AWS Cloud Computing and IAM

(a) AWS Cloud Computing Services (5 Marks, 10-12 points)

1. Definition: AWS offers on-demand IT services like compute, storage, and databases.
2. Compute Services: EC2 for scalable virtual machines, Lambda for serverless
computing.
3. Storage Options: S3 for object storage, Glacier for archival, and EBS for block storage.
4. Database Solutions: RDS for relational databases, DynamoDB for NoSQL, and
Redshift for analytics.
5. Networking: VPC for isolated networks, CloudFront for content delivery.
6. AI/ML: SageMaker for model development and deployment.
7. Security: IAM for access control, GuardDuty for threat detection.
8. Monitoring: CloudWatch provides metrics, logs, and alarms.
9. Developer Tools: CodePipeline for CI/CD workflows.
10. Scalability: Auto-scaling for handling variable workloads.
11. Global Infrastructure: Data centers in multiple regions.
12. Cost Management: Pay-as-you-go model with cost estimation tools.

(b) IAM (Identity and Access Management) (5 Marks, 10-12 points)

1. Definition: AWS IAM manages secure access to AWS resources.

2. Users: Represents individual accounts.
3. Groups: Collection of users sharing permissions.
4. Roles: Temporary access for AWS services or users.
5. Policies: JSON-based documents defining permissions.
6. Authentication: Supports MFA for added security.
7. Granular Permissions: Resource-level access control.
8. Key Features: Centralized access management.
9. Integration: Works with AWS services like S3, EC2.
10. Audit Logging: CloudTrail logs all access and changes.
11. Security: Enforces least privilege policies.
12. No Cost: IAM is a free AWS feature.

2. AWS SPICE and Functions

(a) SPICE Technology in AWS (5 Marks, 10-12 points)

1. Definition: SPICE (Super-fast Parallel In-memory Calculation Engine) powers AWS
QuickSight.
2. Purpose: Optimizes data querying and analysis.
3. In-memory Processing: Stores data in memory for high-speed access.
4. Scalability: Automatically scales for increasing datasets.
5. Security: Data is encrypted in transit and at rest.
6. Cost Efficiency: Reduces costs by minimizing database queries.
7. Real-time Insights: Enables instant updates to dashboards.
8. Integration: Works with various AWS data sources.
9. Durability: Ensures data integrity across failures.
10. Speed: Enables faster visualizations and analytics.
11. Storage: Supports large datasets by leveraging distributed memory.
12. Use Cases: Ideal for BI, reporting, and large-scale analytics.

(b) Functions in Calculated Fields (5 Marks, 10-12 points)

1. ceil() Function: Rounds a number up to the nearest integer.

○ Example: CEIL(4.3) returns 5.
2. concat() Function: Combines multiple strings.
○ Example: CONCAT('AWS', ' Services') returns AWS Services.
3. Types of Functions:
○ Mathematical: SUM(), AVG(), FLOOR(), CEIL().
○ String: CONCAT(), SUBSTRING(), UPPER().
○ Date: NOW(), DATE_ADD(), DAY().
○ Aggregate: COUNT(), MAX(), MIN().
○ Conditional: IF(), CASE().
4. Implementation: Used in dashboards and analytics tools.
5. Purpose: Simplifies complex calculations and data formatting.
6. Performance: Efficient processing for large datasets.
7. Reusability: Functions can be reused in different contexts.
8. Automation: Reduces manual effort in creating calculated fields.
9. Integration: Supports various databases and analytics platforms.
10. Optimization: Improves data query performance.
11. Dynamic Updates: Automatically updates based on data changes.
12. Use Cases: BI tools, dashboards, and reporting.

3. AWS Services

(a) Amazon Glacier (5 Marks, 10-12 points)

1. Definition: Low-cost storage for data archiving.

2. Storage Classes: Instant Retrieval, Flexible Retrieval, and Deep Archive.
3. Durability: 11 9s durability (99.999999999%).
4. Security: Supports encryption for data at rest.
5. Data Retrieval: Configurable retrieval times (minutes to hours).
6. Integration: Works with S3 lifecycle policies for data transfer.
7. Cost: Economical pricing for rarely accessed data.
8. Regions: Available globally with regional redundancy.
9. Use Cases: Archival, disaster recovery, regulatory compliance.
10. APIs: Enables programmatic access for uploading and retrieval.
11. Management: Managed by AWS, reducing overhead.
12. Support: Compatible with multiple AWS tools like SDKs.

(b) Difference Between EC2 and Lambda (2 Marks, 6-8 points)

Feature EC2 Lambda

Server Model Virtual servers Serverless computing

Management Requires manual setup Fully managed

Pricing Pay for uptime Pay per execution

Scaling Manual or auto-scaling Automatic scaling

Use Case Long-running Event-driven tasks

processes

Integration Supports various OS Limited to supported runtimes.

(c) Amazon RDS (3 Marks, 6-8 points)

1. Definition: Relational Database Service for SQL-based databases.

2. Supported Engines: MySQL, PostgreSQL, MariaDB, Oracle, SQL Server.
3. Features: Automated backups, snapshots, and scaling.
4. Security: Uses IAM and encryption for data protection.
5. Integration: Works seamlessly with EC2, S3, and other AWS services.
6. Use Cases: Web apps, analytics, and SaaS platforms.
7. Monitoring: Integrated with CloudWatch.
8. Ease of Use: Fully managed database service.

4. AWS Cloud Migration and Storage Comparison

(a) Cloud Migration Process in AWS (5 Marks, 10-12 points)

1. Assessment: Analyze the current infrastructure, dependencies, and readiness.

2. Planning: Define migration goals, select tools, and estimate costs.
3. Design: Architect the target environment using AWS services.
4. Pilot Testing: Migrate a small workload to test functionality and performance.
5. Data Migration: Use AWS DMS (Database Migration Service) or Snowball for
transferring data.
6. Application Migration: Rehost, refactor, replatform, or rebuild applications on AWS.
7. Security: Implement IAM roles, encryption, and network security in AWS.
8. Scaling: Leverage auto-scaling groups for dynamic workloads.
9. Monitoring: Use CloudWatch and CloudTrail for monitoring and logging.
10. Optimization: Optimize cost, performance, and resource allocation post-migration.
11. Cutover: Transition production workloads to AWS after successful testing.
12. Post-Migration Support: Perform regular updates and maintenance.

(b) Amazon EBS vs. Amazon EFS (5 Marks, 10-12 points)

Feature Amazon EBS Amazon EFS

Type Block storage File storage

Usage Attached to a single EC2 instance Shared across multiple

instances

Performance High IOPS for individual workloads Scalable throughput for

file-based

Cost Model Pay per allocated storage Pay per usage

Scaling Requires resizing Automatically scales

Durability 99.999% availability Regional redundancy

Backup Snapshots available Automatically backed up

Use Cases Databases, applications requiring high Shared file systems, big data
performance

Latency Lower latency Slightly higher latency

Integration Supports EC2 instances only Accessible by multiple

services

Flexibility Fixed size Dynamic scalability

Support Designed for single-instance use Ideal for distributed workloads

5. DynamoDB and Kinesis Stream Architecture

(a) DynamoDB (3 Marks, 6-8 points)

1. Definition: NoSQL database service for fast and flexible data storage.
2. Features: Fully managed, supports key-value and document models.
3. Scalability: Automatic scaling based on workload demands.
4. Performance: Millisecond response times with low latency.
5. Security: Integrated with IAM and encryption options.
6. Use Cases: Gaming, IoT, mobile applications.
7. Backup and Restore: Point-in-time recovery for data protection.
8. Global Tables: Enables multi-region replication.

(b) Architecture of Kinesis Stream (7 Marks, 14-15 points)

1. Definition: Real-time data streaming platform for collecting and processing data.
2. Components:
○ Producers: Generate and send data to Kinesis (e.g., IoT devices, applications).
○ Shards: Logical units for parallel processing in the stream.
○ Consumers: Applications that process or analyze the data.
3. Diagram: Depicts producers, shards, and consumers interacting with Kinesis.
4. Stream Retention: Stores data for up to 7 days.
5. Scaling: Adjusts the number of shards dynamically.
6. Data Ordering: Ensures the order of records within a shard.
7. Processing: Enables real-time analytics or batch processing.
8. Integration: Works with Lambda, Redshift, S3, and Elasticsearch.
9. Security: Data encryption in transit and at rest.
10. Use Cases: Log processing, event monitoring, real-time analytics.
11. Latency: Low latency for immediate data availability.
12. Throughput: High throughput for handling large data volumes.
13. API Access: Provides programmatic access for custom applications.
14. Durability: Replicates data across multiple Availability Zones.
15. Monitoring: Integrated with CloudWatch for metrics.
6. Amazon Redshift, Kinesis, and Snowball

(a) Amazon Redshift (2 Marks, 6-8 points)

1. Definition: A fully managed data warehouse for large-scale analytics.

2. Performance: Uses columnar storage and data compression for fast queries.
3. Integration: Works with S3, DynamoDB, and BI tools.
4. Scalability: Automatically scales to meet demand.
5. Use Cases: Business intelligence, analytics, and reporting.
6. Security: Encryption and IAM integration for access control.

(b) Amazon Kinesis (5 Marks, 10-12 points)

1. Definition: Collects, processes, and analyzes real-time streaming data.

2. Components:
○ Kinesis Streams: Real-time data ingestion.
○ Kinesis Firehose: Delivers data to AWS services like S3 or Redshift.
○ Kinesis Analytics: Processes and analyzes streaming data.
3. Use Cases: Log processing, event monitoring, real-time dashboards.
4. Integration: Compatible with Lambda, S3, and analytics tools.
5. Scalability: Automatically adjusts for data volume.
6. Security: End-to-end encryption and IAM integration.
7. Latency: Low latency for instant processing.
8. Monitoring: CloudWatch provides metrics for throughput and errors.
9. Data Retention: Configurable up to 7 days.
10. Benefits: Improves operational visibility and decision-making.
11. Cost Model: Pay-as-you-go based on data volume and throughput.
12. Reliability: High availability with replication across AZs.

(c) Amazon Snowball (3 Marks, 6-8 points)

1. Definition: A data transfer service for moving large-scale data.

2. Storage Capacity: Ranges from 50 TB to 80 TB per device.
3. Data Encryption: Uses AES-256 encryption during transit.
4. Offline Transfer: Ideal for locations with limited bandwidth.
5. Durability: Rugged design for harsh environments.
6. Use Cases: Disaster recovery, data center migration, IoT data collection.
7. Integration: Supports S3 for seamless data transfer.
8. Cost Efficiency: Reduces network costs and time for large transfers.

Let me know if you need further refinements!