Cloud Data Engineering Quizzes & Antworten – Coursera

Step into the dynamic world of cloud computing with our informative quizzes and expert answers that delve into the intersection of data management and cloud Technologie. Discover key concepts and practices that drive efficient data processing, storage and analysis in cloud Umgebungen. These quizzes serve as a gateway to understanding the principles of cloud computing from data pipelines to scalable architectures.

Whether you are a Daten enthusiast looking to improve your cloud data skills or a technology professional looking to optimize Daten Arbeitsabläufe, this collection provides valuable insights into the world of cloud data Entwurf. Join us on a journey of data innovation as we uncover the intricacies of cloud data management and unlock the potential of scalable and reliable Daten Lösungen.

Quiz 01: Woche 1 Quiz

Q1. What is Moore’s Law?

• A theory on the rise of cloud computing
• A theory on the limits of concurrency
• A prediction about the consistent rise in computing power

Q2. What is one characteristic of a distributed system?

• Serial I/O
• Eventually Consistent
• Lack of concurrency

Q3. Which of the following is a feature of Big Data?

• High-performance disks
• Volumen
• GPU processing

Q4. What problem does the variety of Big Data describe?

• Multiple types of stored data: SQL, Binär, Text, usw
• Amazon RDS
• SQL Database size

Q5 . What Big Data problem does velocity describe?

• DevOps
• Speed data arrives
• Zusammenarbeit

Q6. Why is a Map-Reduce system used to process Big Data?

• Volumen
• Geschwindigkeit
• Parallelization of disk and CPU

Q7. What type of system is Spark?

• Da die Daten in riesigen Mengen mit Milliarden von Datensätzen vorliegen
• Real-time analysis
• Map-Reduce

Q8. What alternatives to CPUs exist to address Moore’s Law ending?

• Async Network
• GPUs
• More threads

Q9. Why would an SSD drive speed up a Relational Database query?

• Faster networking
• It adds more processors
• Faster Disk I/O

Q10. What is an example of a software engineering best practice?

• Hiring “rockstar” developers
• CI
• CD

Woche 02: Cloud Data Engineering Coursera Quiz Answers

Quiz 01: Woche 2 Quiz

Q1. Why is Data Engineering so important in Data Science?

• Only prediction accuracy matters
• It is a bottleneck
• Data Operations are easy

Q2. What is an example of poor Data Governance?

• Storing PII (Personally Identifiable Information)
• Encrypting Data
• Principle of least privilege

Q3. Why is the principle of least privilege a Data Governance best practice?

• It eliminates passwords
• Encryption at rest
• Limits security holes

Q4. What is an example of a serverless data pipeline

• An event triggered from storing data in an S3 bucket
• AWS EC2
• AWS S3

Q5. What is the Python Click framework?

• A web framework
• An ORM
• A decorator oriented Python Command-line tool framework

Q6. Why are Command-line tools essential to automation?

• They compile to C
• They work in Go
• They are often the most simple way to solve a proble

Q7. Why would streaming data present new challenges in Data Science?

• It is encrypted
• Data Drift
• It is static

Q8. Why would an organization want to secure an AWS root account?

• The AWS root account has can any operation
• The password file keeps finding its way to a post it note
• Only the root account can encrypt items

Q9. What is the AWS Shared Security Model?

• The customer and AWS partner
• AWS can fix any security concern
• A customer can decide to maintain the AWS data center

Q10. What is a command-line flag?

• An option to do a new thing
• linting
• testen

Woche 03: Cloud Data Engineering Coursera Quiz Answers

Quiz 01: Woche 3 Quiz

Q1. Why is serverless a vital technological advancement?

• Minimizes the technical overhead in building services
• It is in Python
• The software is “no-code.”

Q2. Why would a developer use AWS Lambda and a Dockerfile?

• Standardized development workflow
• It is a requirement
• It works with Python

Q3. Where can AWS SAM be used?

• AWS EC2
• AWS EBS
• To build serverless applications on AWS

Q4. Why is event-driven programming similar to the lightbulb in your garage?

• It has a programmatic invocation via AWS
• It runs manually
• Lightbulbs respond to multiple signals

Q5. What is an example of an AWS Lambda Trigger?

• Boto3
• API-Gateway
• Python

Q6. What are architectural best practices to contemplate when using serverless?

• Long-running processes
• Connecting to a message bus
• Synchronous design

Q7. Why would you use a command-line tool (CLI) to invoke an AWS Lambda function?

• Rapid prototyping
• To serve out HTTP traffic
• To build a REST API

Q8. What is a good use case for serverless?

• Desktop App
• Long-running process
• Data Engineering

Q9. Why is serverless also called FaaS or Function as a Service?

• It requires Object-Oriented (OO) Programmierung
• A function is the core component of serverless
• It run on bare metal

Q10. Why are containers often involved in serverless architectures?

• Increases Memory
• Containers map well to functions
• Increases CPU

Woche 04: Cloud Data Engineering Coursera Quiz Answers

Quiz 01: Woche 4 Quiz

Q1. What is ETL?

• Extract, Transfer and Load
• Decrypt
• Encrypt

Q2. What is an example of AWS Object Storage?

• EBS
• EFS
• S3

Q3. What is Amazon RDS?

• Amazon Relational Database Service (RDS)
• EFS
• S3 in read only mode

Q4. Why could EFS be a good solution for cluster computing on AWS?

• It works on one machine at a time
• Centralized storage
• It is a database

Q5. What type of AWS Storage can host a website statically?

• S3
• EFS
• EBS

Q7. What does an AWS Step function do

• Stores data in database
• Writes to S3
• Primarily Orchestrates AWS Lambda

Q7. Which of the following is a Cloud Database that uses SQL?

• Elastic Beanstalk
• Amazon DynamoDB
• Google BigQuery

Q8. What is an AI API?

• Überwachung
• An API that uses a pre-trained model
• Instrumentation

Q9. What is an AWS Lambda trigger?

• Instrumentation
• Überwachung
• An AWS Lambda connected to an event

Q10. Why is serverless helpful in Data Engineering?

• Automatic deployment
• Limits complexity
• Automated testing