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- About The Coursera
- About Cloud Data Engineering Course
- Cloud Data Engineering Quiz Answers
- More About This Course
About The Coursera
Coursera, India’s biggest learning platform launched millions of free courses for students daily. These courses are from various recognized universities, where industry experts and professors teach very well and in a more understandable way.
Here, you will find Cloud Data Engineering Exam Answers in Bold Color below.
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About Cloud Data Engineering Course
Welcome to the third course in the Specialization Building Cloud Computing Solutions at Scale! In this course, you’ll learn how to use the Cloud computing ideas you learned in the first two courses of this series to do Data Engineering on real-world projects.
Course Apply Link – Cloud Data Engineering
Cloud Data Engineering Quiz Answers
Week 01: Cloud Data Engineering Coursera Quiz Answers
Quiz 01: Week 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
- GPU processing
Q4. What problem does the variety of Big Data describe?
- Multiple types of stored data: SQL, Binary, Text, etc
- Amazon RDS
- SQL Database size
Q5 . What Big Data problem does velocity describe?
- Speed data arrives
Q6. Why is a Map-Reduce system used to process Big Data?
- Parallelization of disk and CPU
Q7. What type of system is Spark?
- Big Data
- Real-time analysis
Q8. What alternatives to CPUs exist to address Moore’s Law ending?
- Async Network
- 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
Week 02: Cloud Data Engineering Coursera Quiz Answers
Quiz 01: Week 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
Week 03: Cloud Data Engineering Coursera Quiz Answers
Quiz 01: Week 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?
- API Gateway
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) programming
- 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
Week 04: Cloud Data Engineering Coursera Quiz Answers
Quiz 01: Week 4 Quiz
Q1. What is ETL?
- Extract, Transfer and Load
Q2. What is an example of AWS Object Storage?
Q3. What is Amazon RDS?
- Amazon Relational Database Service (RDS)
- 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?
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?
- An API that uses a pre-trained model
Q9. What is an AWS Lambda trigger?
- An AWS Lambda connected to an event
Q10. Why is serverless helpful in Data Engineering?
- Automatic deployment
- Limits complexity
- Automated testing
We will Update These Answers Soon.
More About This Course
By the end of this course, you’ll be able to make Data Engineering applications and use best practices for software development to make them. Among these will be tools for making sure the code is good, logging, instrumentation, and monitoring. Lastly, you’ll use Cloud-native technologies to solve difficult problems in data engineering.
This class is great for both new and more experienced students who want to learn how to use Cloud computing in data science, machine learning, and data engineering. Students should know how to use Linux at a beginner level and Python at an intermediate level. For your project in this course, you will build a serverless data engineering pipeline in the cloud using Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
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