MCQ Answers-Introduction to Designing Data Lakes on AWS Week-1 MCQ answers

This is an Answer set of week-1 of the Edx course Introduction to Designing Data Lakes on AWS Week-1 MCQ Answers, by the code AWS AWS-AWS-OTP-AWSD14.

Introduction to Designing Data Lakes on AWS Week-1 MCQ answers

Introduction to Designing Data Lakes on AWS Week-1 MCQ Answers

Question-1 What is the main value proposition of data lakes?

Options-

  • The ability to ingest and store data that can be the answer for future questions when processed with the right data processing mechanisms.
  • The ability to combine multiple databases together to expand their capacity and availability.
  • The ability to store user generated data (i.e. data from antennas and sensors).

Correct Answer- The ability to ingest and store data that can be the answer for future questions when processed with the right data processing mechanisms.

Question-2 True or false: Data lakes provides the flexibility to use stored data for later use.

Options- TRUE / FALSE

Correct Answer- TRUE

Question-3 True or false: Data lakes are powerful and scalable distributed SQL databases that allow more flexibility and scalability.

Options- TRUE / FALSE

Correct Answer- FALSE

Question-4 Which statements are correct regarding the comparison between data lakes and data warehouses. Select two.

Options-

  • Data lakes are mostly for schema-on-write and data warehouses are mostly for schema-on-read architectures.
  • When doing processing, data lakes offer more choices in terms of language for processing data, while data warehouses are more restricted to using SQL as the query language.
  • Both data lakes and data warehouses can be used combined to better extract insights and turn data into information
  • You are not able to attach data visualization tools to Data Warehouses.
  • Data lakes are not future-proof, meaning that reconfiguration must be done every time new data time is ingested.

Correct Answer- 1. When doing processing, data lakes offer more choices in terms of language for processing data, while data warehouses are more restricted to using SQL as the query language.

2. Both data lakes and data warehouses can be used combined to better extract insights and turn data into information

Question-5 What are main reasons to build a data lake architecture? Select four.

Options-

  • Increase operational efficiency.
  • Make data available from departamental silos
  • Lower transaction costs
  • Offload capacity from databases and data warehouses
  • Provide more storage for data already sitting in the infrastructure.

Correct Answer-

  1. Increase operational efficiency.
  2. Make data available from departamental silos
  3. Lower transaction costs
  4. Offload capacity from databases and data warehouses

Question-6 What is a term for indicating disorganized data lakes lacking curation, management, cataloging, lifecycle/retention, policies and metadata?

Options-

  • Data Mart
  • Data Swamp
  • Data Warehouse
  • Data Lake
  • Data Ocean

Correct Answer- Data Swamp

Question-7 Data lakes make it easy to follow the “right tool for the job” approach. Why?

Options-

  • Yes, because storage can be decoupled from processing and ingestion.
  • Yes, because data lakes can only handle structured data.
  • No, because you are tied to a specific AWS Service.
  • No, because data lakes can only handle structured data.

Correct Answer- Yes, because storage can be decoupled from processing and ingestion.

1 thought on “MCQ Answers-Introduction to Designing Data Lakes on AWS Week-1 MCQ answers”

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