Download Microsoft.DP-100.TestKing.2019-08-22.38q.tqb
| Vendor: | Microsoft |
| Exam Code: | DP-100 |
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Date: | Aug 22, 2019 |
| File Size: | 721 KB |
Demo Questions
Question 1
You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.
You have the following data available for model building:
- Video recordings of sporting events
- Transcripts of radio commentary about events
- Logs from related social media feeds captured during sporting events
You need to select an environment for creating the model.
Which environment should you use?
- Azure Cognitive Services
- Azure Data Lake Analytics
- Azure HDInsight with Spark MLib
- Azure Machine Learning Studio
Correct answer: A
Explanation:
Azure Cognitive Services expand on Microsoft’s evolving portfolio of machine learning APIs and enable developers to easily add cognitive features – such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge. References:https://docs.microsoft.com/en-us/azure/cognitive-services/welcome Azure Cognitive Services expand on Microsoft’s evolving portfolio of machine learning APIs and enable developers to easily add cognitive features – such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/welcome
Question 2
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?
- Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
- Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
- Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
- Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
Correct 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.References:https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning 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.
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning
Question 3
You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.
You have the following requirements:
- Models must be built using Caffe2 or Chainer frameworks.
- Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
Personal devices must support updating machine learning pipelines when connected to a network.
You need to select a data science environment.
Which environment should you use?
- Azure Machine Learning Service
- Azure Machine Learning Studio
- Azure Databricks
- Azure Kubernetes Service (AKS)
Correct answer: A
Explanation:
The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. DSVM integrates with Azure Machine Learning. Incorrect Answers:B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions. References:https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM.
DSVM integrates with Azure Machine Learning.
Incorrect Answers:
B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
