Practice Test

True/False: Amazon S3 is a block storage service suitable for EC2 instances.

  • False

Answer: False

Explanation: Amazon S3 is an object storage service. Amazon EBS is a block storage service suitable for EC2 instances

True/False: Amazon Redshift is optimized for online transaction processing (OLTP).

  • False

Answer: False

Explanation: Amazon Redshift is optimized for online analytical processing (OLAP) not for online transaction processing (OLTP).

Single Select: Which of the following is a durable, block-level storage device?

  • a) Amazon EC2
  • b) Amazon Lambda
  • c) Amazon EBS
  • d) Amazon S3

Answer: c) Amazon EBS

Explanation: Amazon EBS is a high-performance block storage service designed for use with Amazon EC2 for both throughputs and transaction-intensive workloads.

Single Select: What is the main benefit of using Amazon Glacier for data storage?

  • a) Real-time data access
  • b) Low cost data archiving
  • c) High-performance computing
  • d) Object-level storage

Answer: b) Low cost data archiving

Explanation: Amazon Glacier is a secure, durable, and low-cost storage service for data archiving and long-term backup.

True/False: Throughput Optimized HDD (st1) EBS volumes are the best choice for boot volumes.

  • False

Answer: False

Explanation: For boot volumes, General Purpose SSD (gp2) or Provisioned IOPS SSD (io1) are the preferred EBS volume types.

Single Select: Which Amazon service is best suited for Big Data workloads and analytics?

  • a) Amazon Redshift
  • b) Amazon RDS
  • c) Amazon DynamoDB
  • d) Amazon S3

Answer: a) Amazon Redshift

Explanation: Amazon Redshift is a fast, scalable data warehouse that makes it simple and cost-effective to analyze all your data across your data warehouse and data lake.

Multiple Select: What are the benefits of using Amazon S3 for data storage?

  • a) Scalability
  • b) Durability
  • c) Object-level storage
  • d) Real-time data access

Answer: a) Scalability, b) Durability, c) Object-level storage

Explanation: Amazon S3 provides scalable, durable and object-level storage, but it doesn’t provide real-time data access like block storage does.

True/False: SSD storage is always faster than HDD storage on AWS.

  • False

Answer: False

Explanation: SSD storage does not always outperform HDD. Throughput Optimized HDD (st1) and Cold HDD (sc1) can offer higher throughputs than General Purpose SSD (gp2) and Provisioned IOPS SSD (io1).

Single Select: When would you choose Amazon EFS for your application storage?

  • a) When you need a file system that can be shared across multiple EC2 instances.
  • b) When you need to store relational databases.
  • c) When you need to run an operating system.
  • d) When you require object-level storage.

Answer: a) When you need a file system that can be shared across multiple EC2 instances.

Explanation: Amazon Elastic File System (EFS) is a simple, scalable, fully managed elastic NFS file system for use with AWS Cloud services and on-premises resources.

Multiple Select: Amazon DynamoDB is suitable for which workloads?

  • a) High Scale
  • b) High Velocity
  • c) Low latency
  • d) Strategic Analysis

Answer: a) High Scale, b) High Velocity, c) Low latency

Explanation: Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance. It is suited for high scale, velocity and low latency workloads. It is not designed for strategic analysis which involves complex queries and often requires a warehouse solution like Redshift.

Interview Questions

What is schema evolution in the context of data engineering?

Schema evolution refers to the ability to modify a database schema in a manner that does not disrupt the existing data and its associated applications.

What is the main advantage of schema evolutions?

The main advantage of schema evolutions is that they allow developers to modify databases over time in response to changing requirements, without requiring significant downtime or disrupting applications that rely on the database.

Mention one common scenario where schema evolution is required?

Schema evolution scenarios often occur when database tables need to be extended with new columns.

In AWS Glue, how is schema evolution handled?

In AWS Glue, schema evolution is handled by enabling the ‘Update table definition in the data catalog’ option. The schema changes are then automatically handled using the UPDATE and ADD column changes from the source tables.

While working with DynamoDB on AWS, how is schema evolution achieved?

With DynamoDB, schema evolution is simple because it is a schema-less NoSQL database service. You can add or remove attributes from items in a table without altering the table’s schema.

What is the primary challenge in managing schema evolution?

The primary challenge in schema evolution is maintaining the validity and integrity of existing data when altering a database schema.

What is backward compatibility in schema evolution?

Backward compatibility in schema evolution means that new versions of the schema are designed such that they can read, write, and validate instances of data produced by the previous schema versions.

What is Avro’s approach to managing schema evolution?

Avro, a popular data serialization system, deals with schema evolution by storing the schema used to write data alongside the data itself. This method allows data to be read later using a different version of that schema.

What is ‘schema on read’ and how does it aid in schema evolution?

‘Schema on read’ is a strategy that infers schema only when the data is read. This tactic allows for more flexibility, as the data can be stored in a raw form without having to define the schema upfront.

What is ‘schema on write’ and how does it differ from ‘schema on read’?

‘Schema on write’ is a strategy where the schema is enforced when the data is written into the database. While this can ensure consistent data, it offers less flexibility for schema evolution compared to ‘schema on read’.

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