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Question 57 - DP-100 discussion

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Your team is building a data engineering and data science development environment.

The environment must support the following requirements:

support Python and Scala

compose data storage, movement, and processing services into automated data pipelines

the same tool should be used for the orchestration of both data engineering and data science

support workload isolation and interactive workloads

enable scaling across a cluster of machines

You need to create the environment.

What should you do?

A.
Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
Answers
A.
Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
B.
Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
Answers
B.
Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
C.
Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
Answers
C.
Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
D.
Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
Answers
D.
Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
Suggested answer: B

Explanation:

In Azure Databricks, we can create two different types of clusters.

Standard, these are the default clusters and can be used with Python, R, Scala and SQL High-concurrency

Azure Databricks is fully integrated with Azure Data Factory.

Incorrect Answers:

D: Azure Container Instances is good for development or testing. Not suitable for production workloads.

Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning

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asked 02/10/2024
Karol Ligęza
28 questions
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